Cement Production

ABSTRACT

The present invention provides a method and system for manufacturing cement wherein ground particles of cement and calcium sulfate are subjected to infrared sensors, laser sensors, or both, so that emanated, irradiated, transmitted, and/or absorbed energy having wavelengths principally within the range of 700 nanometers to 1 millimeter can be monitored and compared to stored data previously obtained from ground cement and sulfate particles and preferably correlated with stored strength, calorimetric, or other data values, such that adjustments can be made to the mill processing conditions, such as the form or amounts of calcium sulfate (e.g., gypsum, plaster, anhydride), or cement additive levels. The strength and other properties of cement can be thus adjusted, and its quality can be more uniform.

FIELD OF THE INVENTION

The invention relates to cement manufacturing; and, more particularly,it relates to monitoring and adjusting of calcium sulfate and cementadditives in a cement grinding mill to optimize strength of the groundcement.

BACKGROUND OF THE INVENTION

Cement-based materials, such as concrete and mortar, are among the mostwidely used construction materials in the world, as they are necessaryfor making roads, bridges, tunnels, foundations, buildings, dams, andother infrastructure. The manufacture of cement and the study of itsimpact on cement hydration and material strength, however, involveheterogeneous factors that give rise to complex issues.

FIG. 1 illustrates a typical process whereby clinker is made and groundin a mill to provide cement, which is the binder material for concreteand mortar. Raw materials containing calcium, iron, silicon and aluminum(designated at 2), are crushed and blended (4), stored (6), optionallypreheated (8), and fed into the kiln (10), where they are heated to veryhigh temperatures (e.g., 1500° C.). Heating in the kiln is sufficient tofuse the raw materials into clinker “nodules” which are cooled orallowed to cool (12) and are optionally stored (14). The clinker nodulesare added with a source of calcium sulfate (16) and fed into the cementmill (18) which grinds the materials to produce the finished cement(20).

Supplemental cementitious materials, such as fly ash, slag, otherpozzolans, and/or limestone, may be added with the clinker before (at16) or after the grinding mill stage (18). The produced cement istypically cooled and then tested (20), stored in silos (22) until beingdelivered to the customer (22), who uses the cement to make concrete,mortar, or other construction materials.

Typically, sulfate, in the form of gypsum, is added into the cement mill(18), where the clinker and gypsum are ground to a specific particlesize (20). The resultant ground particles of clinker, and gypsum arecommonly referred to as Portland cement. Blended cements are Portlandcements combined with supplementary cementitious materials (e.g., flyash) before or after the mill.

The manufacture of Portland cement generates a significant amount ofcarbon dioxide. This occurs especially during firing of the kiln (10)where calcination of the limestone occurs (releasing carbon dioxide).For each metric ton of cement produced, approximately 0.84 tons ofcarbon dioxide are released (See e.g., WBCSD Cement SustainabilityInitiative reports). As annual production is about 4 billion metric tonsof cement, this amount represents approximately 5% of all carbon dioxidegenerated by man-made processes. Reducing carbon dioxide is of greatimportance to sustainability initiatives in cement production.

It can be difficult to obtain consistent quality in cement productsdespite expensive process controls. Major reasons include highvariability of the raw materials (due to their origin within a givenquarry as well as across multiple quarries) and of processingconditions—such as kiln temperature, oxygen levels within the kiln, rateof cooling, and kiln fuel changes that can affect the interaction ofchemical constituents as the clinker is formed.

The present inventors believe that improving control over cementhydration, despite numerous factors that fluctuate during manufacturing,such as aluminate content and sulfate availability, provides manybenefits. They propose to implement monitoring and adjustment processesnot currently used or envisioned today in the cement manufacturingfield, so that greater consistency of cement product quality can berealized.

By focusing on consistency by accounting for the variation in clinker,sulfate and other materials introduced into the cement mill as well asthe grinding process conditions, the present inventors believe that theycan enhance the consistency of strength in the cement product, as wellas reduce its large carbon footprint.

Furthermore, the inventors believe that the performance of cementadditives can also benefit by accounting for variation in the clinkerand other materials introduced into the cement mill as well as thegrinding process conditions. Cement additives are chemical products usedto improve the efficiency of cement grinding mills (grinding aids)and/or to improve the performance of mortars and concretes made with thecement (quality improvers). One such performance parameter is cementcompressive strength. Cement additives are often used to increase thestrength of the cement at one or more ages. FIG. 2 shows some typicalresponse curves of compressive strengths obtained by using the testingmethods described in EN-196-1:2016 on mortars as a function of twostrength enhancing chemicals commonly used in cement additives. As canbe seen, different cement additives have different optimum dosagerequirements with respect to achieving optimum cement strength (in thiscase 1 day compressive strength). Typically, the dose of a cementadditive is determined based on the production parameters of the mill(such as mill output) and quality parameters of the cement (such asfineness, residue in the “#325 sieve”, powder flow, pack-set, set time,rheological behavior, and compressive strength). Most plants areequipped with flowmeters that allow accurate monitoring of the volume ofcement additive being introduced in the mill. Cement additives can beused to further reduce the inconsistencies and to improve the quality ofthe cement. Knowledge of the variations can allow cement additives to beadjusted in type or amount, with a variety of goals including, but notlimited to, maximizing strength, achieving a target early age strengthwithout exceeding a later age maximum, increasing the use ofsupplementary cementitious materials, controlling set time or rheology,and other advantages. Thus, within a closed-loop framework, additivescan be used to increase consistency of the final cement product.

Ground Portland cement is primarily composed of hydratable calciumsilicates. The calcium silicates are essentially a mixture of tricalciumsilicate (otherwise referred to as alite, 3CaO.SiO₂, or “C₃S” in cementchemists' notation) and dicalcium silicate (otherwise referred to asbelite, 2CaO.SiO₂, or “C₂S”) in which the former is the dominant form,with lesser amounts of tricalcium aluminate (3CaO.Al₂O₃, “C₃A”) andtetracalcium aluminoferrite (4CaO.Al₂O₃.Fe₂O₃, “C₄AF”). See e.g.,Dodson, Vance H., Concrete Admixtures (Van Nostrand Reinhold, New YorkN.Y. 1990), page 1.

In order to control the early calcium aluminate reaction, cementmanufacturers typically add an amount of sulfate, often in the form ofgypsum, to the cement clinker. It is the sulfate which, upon contactwith water when mixed with cement (e.g., to make concrete or mortar),reacts with calcium aluminate to form a hydrated product calledettringite. This reaction consumes aluminates and thus lowers aluminumconcentration in solution, which allows proper formation of the calciumsilicate hydrates (C—S—H) and thereby confer strength to the concrete ormortar made from the cement.

The present inventors shall use calcium sulfate as an example of a“source of sulfate” which will be introduced into a grinding mill alongwith clinker to produce cement. Gypsum (i.e., calcium sulfate dihydrate)is a form of calcium sulfate that reacts readily with calcium aluminatein the cement during hydration. Other forms of calcium sulfate are“plaster” (e.g., calcium sulfate hemihydrate, or basanite), and calciumsulfate anhydrite. Thus, gypsum is 1 mole of calcium sulfate associatedwith 2 moles of water (Ca₂SO₄.2H₂O); plaster is 1 mole of calciumsulfate associated with 0.5 moles of water (Ca₂SO₄.½H₂O); and anhydriteis calcium sulfate that is not associated with water (Ca₂SO₄).

The hemihydrate form of calcium sulfate (plaster) is also relied upon asa calcium sulfate source in the cement plant to control the aluminatereaction. The advantage of using hemihydrate is due mainly to its fastersolubility in water. Although plaster is rarely added as a source ofsulfate directly into the mill, varying amounts of calcium sulfatehemihydrate are present in the finished cement as a result of thedehydration of gypsum (the dihydrate form). This dehydration is promptedby high temperatures (e.g., above 100° C.) in the grinding millenvironment that tend to evaporate water from gypsum and convert it intoplaster.

In spite of attempts to control temperature and relative humidityconditions in the milling system, cement plant owners are not readilyequipped to control precisely the amount of plaster being transformedfrom gypsum within the grinding process. This transformation is commonlyseen in ball mill systems that readily generate heat; but not typicallyseen in vertical roller mills (VRMs) wherein the temperature of the millis typically lower than the dehydration temperature of the gypsum, andadditionally, the humidity is relatively higher, due primarily fromwater being added to stabilize the VRMs. Both conditions lead todecreased dehydration of gypsum to plaster.

Calcium sulfates can react with the aluminate phases to form ettringite,thus decreasing calcium aluminate hydration that otherwise decreasesworkability and strength of the cement. Although calcium sulfates canbalance the aluminate reaction by keeping the sulfate concentration highenough to limit aluminate reactions in advance of the silicate reactionsto prevent flash set and poor strength development (through hindrance ofthe calcium silicate reaction), a number of generally accepted standardsin the industry (e.g., ASTM C1157, EN 197-1:2011) impose limits on totalsulfate content. Such standards impose limits on the maximum amount ofsulfate in cements under the theory that excessive sulfate levels giverise to detrimental expansion and false setting of cements. Otherstandards have evolved to permit higher sulfate levels as long asdeleterious expansion is avoided (e.g. ASTM C150/C150M-18 does not limitthe sulfate as long as tests under ASTM C1038/C1038M-14b do notdemonstrate deleterious expansion).

Thus, an optimum amount of sulfate is desired to control the calciumaluminate reaction while maintaining performance factors such asstrength, workability shrinkage, and expansion.

Despite the importance of adding the optimum amount of sulfate, testingfor optimum sulfate levels in the grinding mill is typically done on aninfrequent basis. Strength testing requires at least 24 hours, whilecalorimetric testing requires 8-24 hours. See e.g., Sandberg, P. “Theuse of isothermal calorimetry in cement production,”http://downloads.calmetrix.com/Downloads/CCW2016/Paul_Sandberg_The_use_of_Isothermal_calorimetry_in_cement_production.pdf).

Given that large cement plants can produce 10,000 metric tons (MT) ofcement every day, the present inventors believe that processingconditions (e.g., quality and ratio of raw materials fed into the kiln(10), the fuel used for heating the kiln, and other factors) present toomany variables for the typical cement manufacturer to consider atpresent time.

The present inventors believe that a consistent quality of cement cannotbe attained by adjusting sulfate levels annually, semi-annually or evenmonthly, because variations in the clinker over shorter time incrementscan alter the ideal sulfate level for reaching maximum strength at agiven age of the cement.

In preparing for summary of the present invention, which culminates inthe next section, the present inventors describe specific difficultiesin testing the relationship between sulfate levels and optimum strengthin cement, as well as current practices which have tended to maskdiscovery and resolution of those difficulties to this point in time.

FIG. 3A illustrates compressive strength data (at 1 day age) for cementcontaining various amounts of gypsum (dihydrate form). The gypsum isadded incrementally into ground cement clinker in accordance with ASTMC563-17, and is dosed as a percentage of the cement mass. The cementmade from variously dosed gypsum levels is used to form mortar testsamples, which are crushed to obtain compressive strength values, inaccordance with ASTM C109/109M-16a or EN-196-1:2016. The results shownin FIG. 3A are made in accordance to EN-196-1:2016.

The strength curve data of FIG. 3A suggests that the cement has optimum1 day compressive strength when sulfate (in the form of gypsum) is addedto the cement clinker in the amount of 1.5%-2.0% based on weight ofcement.

Compared to compressive strength testing, calorimetric testing of cementsamples using varying amounts of sulfate is undoubtedly more convenient.FIG. 3B graphically illustrates cumulative heat output testing, over aperiod of 24 hours, of hydrating cement samples containing gypsum (thedihydrate form) in varying amounts. According to the data illustrated inFIG. 3B, the optimum sulfate content (gypsum) for achieving maximumcumulative exothermic value in the cement is approximately 1.5%-2.0%based on the weight of the cement, essentially giving the same result asthe compressive strength tests.

The present inventors note that, to this point in time, a processmanager or the quality control manager of a cement clinker grinding millwould typically determine optimum sulfate content using a procedure suchas the one described in ASTM C563-17. A small number of mortar sampleswith varying amounts of gypsum are formed into test samples which arecrushed to obtain strength data (e.g., ASTM C109/109M-16a,EN-196-1:2016). FIG. 3C illustrates a typical four point curve usingthis conventional method. A mill operator might estimate, using such asmall number of samples (for compressive strength testing or forcalorimetric testing) that the optimum amount of sulfate (e.g., gypsum),for example, is 1.75% based on weight of cement. Based on this data, themill operator would tend to set the level of gypsum addition in the millat this amount for an extended amount of time, (e.g. the next 12months).

However, the present inventors believe this conventional approach doesnot guarantee optimum strength because clinker components, kiln fuel, aswell as the form or amount of sulfate likely fluctuate over the 12 monthperiod and potentially on the daily and hourly periods. They alsobelieve that optimum strength of the cement cannot be achievedconsistently based on this conventional practice.

As explained in the background, the present inventors realize that theheat of the mill conditions could transform gypsum (dihydrate form) tothe plaster form, which is more soluble (hemihydrate form). They alsorealize that the humidity levels in and around the mill could fluctuategreatly throughout any extended period of time, such that the amount ofrapidly available sulfate could fluctuate.

Indeed, the present inventors believe that the amount of sulfatecontained in the clinker itself, an amount of sulfate which albeit istypically small, can vary substantially and become a factor influencingstrength of the cement at some point within any extended period of time(e.g. 12 months).

The present inventors believe that mill operators do not usually domulti-point compressive strength or calorimetry testing with enoughfrequency to obtain useful information regarding sulfate content andrelative strength at certain ages; and that they do not routinelyconsider the myriad process conditions that change from moment to momentand that affect cement properties.

Although it is possible in a laboratory setting to measure sulfatelevels in cement using X-Ray Diffraction (XRD) or X-Ray Florescence(XRF) after the cement is ground, there is no method to calculate theoptimum gypsum (calcium sulfate dihydrate) or plaster (calcium sulfatehemihydrate) content based on XRF or XRD data.

Furthermore, there is no method that is used in the cement industry foradjusting the amount of dihydrate and hemihydrate forms of calciumsulfate to obtain optimum strength for certain cement ages. As a result,cements being produced today can demonstrate large fluctuations in termsof quality (e.g. set time and strength), despite investments in qualitycontrol systems by the cement manufacturers.

Cement manufacturers have attempted to mitigate the risks stemming fromthe variabilities of cement production by “overdesigning” their cementproducts. For example, this might be done by using more clinker and lesssupplemental cementitious materials (e.g., fly ash, slag) or by grindingcement particles to finer Blaine specific surface areas to increase theaverage compressive strength and make it less likely that strengthfluctuation result in the cement not meeting specification. In eithercase, these approaches involve higher carbon dioxide generation (due toclinker kiln operation or milling electricity) and are not energyefficient.

Concrete producers also have used more cement to overcome inconsistentstrength performance. Up to twenty percent extra cement might be used toensure that strength targets are met. This again means more carbondioxide is generated due to the greater demand for cement.

SUMMARY OF THE INVENTION

In surmounting the disadvantages of prior art approaches, the presentinvention addresses several issues in providing a method and system foroptimizing sulfate and cement additive levels, cement fineness and otherfactors to attain target strength (at certain ages) or other performancetargets when the cement is hydrated.

The present inventors take into consideration that (A) clinkercomponents vary (e.g., ratio of calcium (from limestone), iron, silica,aluminate); (B) nature and type of kiln fuel varies (e.g., coal,municipal waste, recycled tires, etc.); (C) kiln conditions vary (e.g.oxygen levels, flame length, etc.); and that (D) the amount of availablesulfate can vary due to the hydration state of calcium sulfate beingintroduced into the grinding mill. For example, gypsum can dehydrateinto plaster due to the hot environment of the grinding mill, wherebythe calcium sulfate is rendered more soluble; and, hence, sulfate ismore rapidly available for use in balancing the aluminate reactions.

As illustrated in FIG. 3D, cements ground from three different clinkers,having different components and/or component ratios, are shown torequire different sulfate contents (added as gypsum) to achieve amaximum 1-day strength. The present inventors believe this type ofbehavior can be found not only across various cement plants, but alsowithin the individual manufacturing process of a single cement plantover a relatively short period of time.

Likewise, FIG. 4 shows the responses of three different cements (C1, C2,C3) to the addition of a given cement additive. FIG. 4 illustrates thatthe impact of cement additives on the strength of a cement depends onseveral characteristics of the cement that include its chemical andmineralogical composition and its physical properties. In this case, theBlaine specific surface area, which is an indication of the surface areaof the cement, is held constant. Even as such, the differences in C1, C2and C3 are a result of the respective clinker chemistry differences.

In summary, any given cement plant can have a significant fluctuation inthe raw materials, kiln fuels and kiln operating conditions used formaking cement clinker. Given this scenario, the present inventorsbelieve that a mill owner (cement manufacturer) must not simply performstrength or calorimetric testing infrequently (e.g. just once a year)and rely on those test results for an extended period of time to makecement with a consistent quality.

Aside from frequent monitoring of the optimum sulfate, the presentinventors also believe that the amount and form of calcium sulfateexisting in the cement should be monitored and adjusted on a frequentbasis, as this would help to minimize variation in the quality andperformance of the cements. More preferably, the relative amounts ofboth calcium sulfate dihydrate (gypsum) and calcium sulfate hemihydrate(plaster) should be monitored and adjusted on a frequent basis. Doing sowould permit a mill operator to take into consideration the effect ofvarious changing environmental conditions, including plant and storageconditions, which can affect the source of calcium sulfate and levels ofsoluble sulfate available to control the aluminate balance, which, inturn, can affect cement performance.

Accordingly, in an exemplary embodiment, the present invention providesa method for manufacturing cement, comprising:

(A) introducing, into a grinding mill, raw materials comprising clinker,a source of sulfate chosen from gypsum, plaster, calcium anhydrite, or amixture thereof, and optionally one or more supplemental cementitiousmaterials and optionally at least one cement additive; grinding the rawmaterials, to produce a ground blend of particles comprising groundclinker and calcium sulfate; and separating the ground blend ofparticles within a classifier whereby a first portion of the particlesor the finished cement are sent to a silo or other receptacle forcontaining the finished cement and whereby a second portion of theparticles is recirculated into the grinding mill for further grinding;

(B) providing at least at least one sensor system chosen from infraredsensor system, laser diffraction sensor system, or both, and detectingemanation, reflectance, transmittance, or absorption of energy by orthrough the ground blend of particles or finished cement provided instep (A), and generating output signals corresponding to the detectedenergy;

(C) comparing output signals generated in step (B) to data stored inprocessor-accessible memory, the stored data comprising output signalvalues previously obtained from sensors measuring the emanation,reflectance, transmittance, or absorption of energy in the infraredspectrum, laser diffraction spectrum, or in both the infrared and laserdiffraction spectrums, the stored data being correlated with a physicalor chemical property of the corresponding finished cement, hydratedcement, or cementitious product made with the cement; and

(D) in response to the comparison in step (C), adjusting (i) amount,form or both amount and form of calcium sulfate introduced into thegrinding mill in step (A); (ii) classifier settings, thereby to changerelative amounts of ground particles being sent to the silo and beingrecirculated back into the grinding mill; (iii) amount, type, or bothamount and type of cement additives introduced into the grinding mill;(iv) amount of water being introduced into the grinding mill; (v) amountof air provided by adjusting power or speed of a fan or blower connectedto ventilate the mill; (vi) amount or type of supplemental cementitiousmaterial introduced into the grinding mill; (vii) cement cooler setting,thereby to change the temperature of the finished cement or (viii)combination of any of the foregoing.

In further exemplary methods of the present invention, the amount andform of sulfate can be adjusted by taking into account (A) the totalamount of calcium sulfate (i.e. gypsum, plaster and anhydrite) as wellas (B) the ratios between each of the different forms monitored in theground blend of particles or finished cement, and to adjust both (A) and(B) on a periodic basis. For example, monitoring and adjustment canoccur monthly intervals or less.

In still further exemplary methods, the present inventors believe thateven further advantages may be achieved through monitoring and adjustingthe source of calcium sulfate (i.e., amount and/or form) in the groundblend of particles or finished cement on a more frequent basis, such ashourly, more preferably every fifteen minutes, and most preferably at aninterval less than or equal to 5 minutes.

In still further exemplary methods of the present invention, the amountand type of chemical additive introduced into the mill can be adjustedon a periodic basis based on the monitoring and analysis of the groundblend of particles or finished cement.

The present invention also provides a cement grinding system which isconfigured to accomplish the exemplary method as described in thepreceding paragraph. The cement grinding system comprises a mill and atleast one IR sensor for monitoring sulfate levels in particles ground inthe mill, the at least one IR sensor being in communication with aprocessor configured or programmed to monitor IR wavelengths reflectedfrom particles ground in a cement grinding mill.

Further advantages and features of the invention will be discussedfurther hereinafter.

BRIEF DESCRIPTION OF DRAWINGS

An appreciation of the benefits and features of the invention may bemore readily appreciated when the various sections of this specificationare considered in conjunction with the drawings.

FIG. 1 is a flow diagram illustration (PRIOR ART) of clinker kiln andcement mill in the manufacture of cement (as discussed in the Backgroundsection).

FIG. 2 is a graph illustration of 1 day compressive strength of twocements as a function of varying levels of cement additives (asdiscussed in the Background section).

FIGS. 3A, 3B, and 3C are graph illustrations of data points obtainedusing conventional methods for optimizing sulfate levels in cement (asdiscussed in the Background section).

FIG. 3D is a graph illustration of one-day compressive strength as afunction of varying levels of total sulfate in three cements (asdiscussed in the Summary section).

FIG. 4 is a graph illustration of varied performance when using the samecement additive in three different cements having same Blaine specificsurface area (as discussed in the Background section).

FIGS. 5A through 5E are graph illustrations of exothermic heat value(heat flow) as a function of time in five different samples of hydratingcement, demonstrating peak exothermic values corresponding to peak C₃Sreaction and the visible onset of the renewed or completed C₃A reactionin the cement.

FIG. 6A is a graph illustration of one-day compressive strength as afunction of exothermic values (cumulative heat) over 24 hours afterwater has been mixed into three cements to hydrate the cements, wherethe maximum strength for each cement is designated by the square symbol.

FIG. 6B is a graph illustration of one-day compressive strength as afunction of the difference in the peak exothermic values whichcorrespond to C₃S and C₃A dissolution in three cements, where themaximum strength for each cement is designated by the square symbol.

FIG. 7 is a graph illustration demonstrating weight loss over time andthe derivative of the weight loss with respect to temperature for acement sample obtained using a thermogravimetric analysis instrument.The cement sample is exposed to a temperature ramp from 22° C. to 450°C.

FIG. 8 is a flow chart of an exemplary method of the present invention.

FIG. 9 is a diagram illustration of an exemplary system of the presentinvention.

FIGS. 10A through 10D are graph illustrations of the relationship ofinfrared (IR) light intensity (obtained from cement samples) as afunction of IR wavelength, and their derivatives.

FIG. 11 is a graph illustration demonstrating the prediction accuracy ofa model that receives an NIR signal spectra and that provides apredicted optimum Delta value, wherein the data plot confirms aone-to-one correlation (illustrated by the solid straight line) across awide range of clinker chemistries and Blaine specific surface areas.

FIG. 12 is a graph illustration demonstrating the prediction accuracy ofa model that receives an NIR signal spectra and that provides apredicted Delta value, wherein the data plot confirms a one-to-onecorrelation (illustrated by the solid straight line) across a wide rangeof clinker chemistries and Blaine specific surface areas.

FIG. 13 is a graph illustration demonstrating the prediction accuracy ofa model that receives an NIR signal spectra and outputs a predicted 1day strength value, wherein the data plot confirms a one-to-onecorrelation (illustrated by the solid straight line) across a wide rangeof clinker chemistries and Blaine specific surface areas.

FIG. 14 is a graph illustration demonstrating the improved predictionaccuracy of a model that receives an NIR signal spectra and outputs apredicted 1 day strength value, wherein the data confirms a one-to-onecorrelation (illustrated by the solid straight line) for a singleclinker chemistry.

FIG. 15 is a graph illustration demonstrating the improved predictionaccuracy of a model that receives an NIR signal spectra and outputs apredicted 1 day strength value, wherein the Delta is between 1.5 and 2.5hours, and, furthermore, wherein the data plot confirms a one-to-onecorrelation (illustrated by the solid straight line).

FIG. 16 is a graph illustration demonstrating the compressive strengthresponse of Cement 1 sulfated at three different levels and exposed tofour levels of a cement additive comprising Na₂-EDG.

FIG. 17 is a graph illustration demonstrating the compressive strengthresponse of Cement 2 sulfated at two different levels and exposed tofour levels of a cement additive comprising DEIPA.

FIG. 18 is a graph illustration demonstrating the compressive strengthresponse of Cement 3 sulfated at two different levels and exposed tofour levels of a cement additive comprising DEIPA.

FIG. 19 is a graph illustration demonstrating the compressive strengthresponse of Cement 4 sulfated at two different levels and exposed tofour levels of a cement additive comprising DEIPA.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

As used herein, the term “cement” means and refers to hydratable cement,such as Portland cement, which is produced by grinding clinkerconsisting of hydraulic calcium silicates, aluminates, andaluminoferrites, and one or more forms of calcium sulfate (e.g., gypsum)as an interground addition. Frequently, Portland cement is combined withone or more supplemental cementitious materials as well as cementadditives, and provided as a blend, all of which binds aggregatestogether to make a mortar or concrete.

The term “cement additive” means and refers to a chemical product oforganic and/or inorganic nature that is added during the manufacture ofcement either into the grinding mill, at the entrance of the separatoror at the separator exit. Cement additives comprising grinding aids willprimarily reduce the agglomeration of fine particles during the grindingprocess, and as a result, will increase the efficiency of the grindingmill. Cement additives comprising quality improvers or strengthenhancers will primarily increase the strength of the cement duringhydration. Strength can be enhanced at early ages (e.g. 1 day) or laterages (e.g. 28 days), and intermediate ages as well. Some chemicaladditives provide both early and later age strength enhancements.Frequently, chemical additives provide some level of both grindingenhancement and strength enhancement. Cement additives also refer to anychemical added during the cement manufacturing process that enhances anyproperty of the cement such as, but not limited to: set time, shrinkage,expansion, workability, concrete admixture compatibility, etc.

The term “concrete admixture” means and refers to chemicals added duringthe manufacture of concrete.

As used herein, the phrase “supplemental cementitious materials” meansand includes fly ash, silica fume, granulated blast furnace slag,limestone, clay, calcined clay, natural pozzolans, or mixtures thereof(“SCM”). These SCMs by themselves often have little or no cementitiousproperties, but, when blended with Portland cement and mixed with water,the blended cement and SCMs can bind aggregates together to make mortar,concrete, or other hydratable cementitious compositions.

The term “aggregate” means and refers to sand and/or stone (or crushedgravel) particles, typically having average size of 0.5 to 50 mm.Aggregates may also comprise calciferous, siliceous or siliceouslimestone minerals. Such aggregates may be of either the “natural” type(e.g., derived from glacial, alluvial, or marine deposits which aretypically weathered such that the particles have smooth surfaces) or maybe of the “manufactured” type, which are made using mechanical crushersor grinding devices. Coarse aggregate stone particles are typicallygrouped into various size fractions as described for instance in ASTMC33-16e. As the size fraction used is controlled by various factors,such as the space between reinforcing bars in a proposed construction,aggregate size is often considered in concrete mix designs. The term“aggregate” may also be used to refer to crushed returned concrete (e.g.“recycled aggregate”).

As used herein, the term “mortar” will refer to a mixture of cement andoptionally supplemental cementitious materials such as limestone, flyash, granulated blast furnace slag and other pozzolanic materials,water, and fine aggregates (e.g., sand). The term “concrete” is a mortarfurther containing a coarse aggregate, such as gravel or crushed stone.Mortars and concretes may optionally contain one or more chemicaladmixtures for modifying the hydratable cementitious composition in itsplastic or hardened state (e.g., plasticizers for increasingworkability, set accelerators, set retarders, air entrainers, airdetrainers, plastic shrinkage reducing admixtures, corrosion inhibitors(for steel reinforcing bars within the concrete)).

As used herein, the phrase “a source of calcium sulfate” means andincludes gypsum, plaster, and the anhydrite form of calcium sulfate. Theterm “gypsum” refers to the dihydrate form of calcium sulfate. Gypsumoccurs as a natural mineral or by-product from industries. Whensubjected to sufficient heat, gypsum (more precisely CaSO₄.2H₂O)dehydrates to form calcium sulfate hemihydrate (CaSO₄.5H₂O) also knownas “plaster.” The mineral form of calcium sulfate hemihydrate is calledbasanite. The complete dehydration produces calcium sulfate anhydrite(CaSO₄). Natural gypsum sources may contain impurities from othermineral such as quartz, calcite, dolomite, anhydrite, clays fromdeposits. The “gypsum” used in cement plants can also be obtained fromchemical by-products such as phosphorgypsum (or phosphogypsum) fromphosphoric acid manufacture, fluorogypsum from hydrofluoric acidmanufacture, formogypsum from formic acid manufacture, desulphogypsum(or FGD™ brand gypsum) from flue gas desulphurization, etc. By-productgypsum can contain impurities that can affect the cement performance.Calcium sulfate dihydrate is commonly added to Portland cement clinkerto control the set time and strength development of the cement.

At the optimum sulfate level for the particular cement, the rate ofaluminate reactions are slowed in order to minimize their interferencewith the silicate reactions, thus allowing the strength of the cement tobe optimized.

As used herein, the term “undersulfated” means that the level of sulfateadded to the cement is below the optimum sulfate required to maximizethe cement strength. Furthermore, severely undersulfated cement couldcause “flash setting,” referring to rapid loss of workability, largeheat release, and dramatic loss of early strength development. In othercases, the undersulfated condition can lead to extended set and lowstrength gain development and poorer slump retention. Undersulfatedconditions can also lead to problems with admixture performance, in partdue to absorption of the admixture into certain hydrating aluminatephases.

As used herein, the term “oversulfated” means that the level of thesulfate added to the cement is above the optimum sulfate required tomaximize the cement strength. Amounts greater than that required toprevent the aluminates from interfering with the silicate hydrations donot help. Strength will go down further sulfate is added, sometimessharply.

A second condition exists relative to higher sulfate levels, known asfalse set. This occurs when gypsum is dehydrated to form plaster (whichdissolves faster), and there is relatively low aluminate activity to useof the sulfate that has dissolved. In this case, the plaster reformsinto gypsum as crystals which physically lower the workability of thehydrating cement, generally in the first few minutes. While this doesnot directly impact the strength, addition of water to overcome thereduced workability results in a overall lower strength.

As used herein, the term “hydration” means and refers to the hydrationof Portland cement which is a sequence of overlapping chemical reactionsbetween clinker components, calcium sulfate and water, leading tosetting and hardening. Cement hydration is most typically studied usinga calorimeter to monitor heat released during hydration. Isothermalcalorimetry is a particularly useful way to follow the progression ofthe cement hydration, which is the result of several simultaneousexothermic reactions. The major chemical reactions between clinkercomponents and calcium sulfate in the cement, and water initiate thehydration process after water is mixed with the cement. The words“hydrated” or “hydration” may include the fact that cement is stillcuring or increasing in strength (e.g., compressive strength) over time.

In the cement and concrete industries, it is an understanding thatOrdinary Portland cement (OPC) “prehydrates” during storage or handlingin moist environments, forming hydration products on or near itsparticles' surfaces. Thus, the term “prehydration” is something of anoxymoron, since what is being referred to is unwanted hydration (orwater bonding or reacting at the surface of cement particles) prior tothe time at which the cement is used in concrete and mortar incombination with water and hardened into a mass or structure. Again, theterm “prehydration” means and refers to an undesirable reaction betweensoluble components of cement (or its various phases) and moistureabsorbed onto the surface of the cement particles either from liquidwater or directly from the vapor phase that occurs before the cement ismade into mortar or concrete upon mixing with hydration water (in amountsufficient to initiate hydration whereby concrete hardens into rock-likemass or structure). The level of prehydration of the cement can bequantitatively measured, for example, using analytical methods wherebythe amount of water that is chemically bound to the particle surface isascertained. Further detailed explication follows below.

Prehydration changes the surface of the cement particles, limiting therate of dissolution which leads to a delay of setting, strengthdevelopment and poorer flow properties. The surface change can alsointerfere with the action of chemical additives, rendering them lesseffective in some cases. Thus, it may be difficult to mitigate effectsof prehydration reactions set time by using accelerators, for example.It is only necessary that a very small fraction (much less than 1%) ofwater taken up relative to cement mass will lead to negative effects ata later stage.

The most common adjustment made by cement plants in response toprehydration due to surface water reactions is to grind the cementparticles to a higher fineness, to offset strength loss that typicallyoccurs. This has well-known disadvantages, however, such as increasedenergy consumption, decreased throughput, and increased water demand forthe finished cement. In summary, the prehydration of the cement can havequite significant effects on the properties of the cement once it isused to make concrete or mortar, and mitigating these effects after theprehydration reactions have occurred can be difficult.

Prehydration of the cement can be measured by heating a cement sampleand measuring the weight loss within a defined temperature range. Thelevel of prehydration reactions on the cement particle surfaces is mostaccurately measured using a thermogravimetric analysis (TGA) instrument.The amount or level of prehydration reactions on the cement particlesurfaces is quantified for the present purposes as the parameter Wk,defined as the percentage mass loss of a cement sample as it is heated,starting at a temperature just after the completion of the gypsumdehydration and finishing at a temperature just before the calciumhydroxide (portlandite) starts to decompose. Chemically bound waterstarts to be released at temperatures as low as 60° C. and can continueuntil temperatures as high as 600° C. The Wk parameter measures thechemically bound water in a region of the weight loss versus temperaturecurve where only strength-giving clinker phases are dehydrating. Atlower temperatures, there is also the dehydration of the added calciumsulfate phases and release of physically bound water; at highertemperatures, there is also the dehydration of calcium hydroxide fromfree lime and decarbonation of carbon-containing phases.

As used herein, the term “age” as it is used with respect to acementitious composition refers to the time elapsed since the momentthat water is mixed into the cement, mortar, or concrete to initiate thehydration of the cement, whereby the cement (when used to produceconcrete) is hardened into a mass or structure. For example, strengthproperties may be measured at 1, 2, 3, 7, and/or 28 days (or at other“ages”) after mixing with water. Different ages may have significancefor different cement producers, and thus an optimum sulfate may refer tothe sulfate required to optimize strength at a given age (e.g. 1 day, 28days, etc.).

The major chemical reactions in cement during hydration are commonlyidentified in terms of five kinetic stages, as follows. These stages aremost commonly observed via isothermal or semi-adiabatic colorimetry.Stage 1 represents primarily the rapid dissolution of clinkerinterstitial phases (including an initial dissolution of a fraction ofthe C₃A) and formation of ettringite or other aluminate reactionproducts. Hemihydrate dissolves, and gypsum or syngenite may form. Stage2 is known as the induction period, which is characterized by a slowdownof the heat released. Stage 3 corresponds to the acceleration periodwhen silicate hydrates begin to form i.e. C—S—H and CH. Stage 4 ischaracterized by the slowdown of the heat, which becomes even lower atthe Stage 5. Although all cements hydrate when mixed with water, eachstage of hydration can have a different rate, depending on multipleparameters, including but not limited to: cement chemistry, temperature,reactivity, water/cement ratio, presence of cement additives, etc.

FIGS. 5A to 5E illustrate different hydration curve scenarios. Thehydration behavior of a cement having a balanced sulfate content isshown in FIG. 5C. The solid line represents the heat flow, or rate ofheat released by the cement system, over time. The dotted linerepresents the second derivative of the heat flow. In this set offigures (FIGS. 5A-5E), the heat flow is normalized and centered (i.e.the mean of the signal is subtracted from the signal and the result isdivided by the standard deviation of the signal). In FIG. 5C, both thepeak exothermic value corresponding to maximum C₃S dissolution reactionrate (which is noted by the “X” symbol, appearing at the peak) and thevisible “onset” of the renewed C₃A dissolution reaction (which isrepresented by the “|” symbol appearing in the valley between peaks) areshown. Those skilled in the art will appreciate that the hydrationcurves (shown as solid lines) in FIGS. 5A-5E are summations orcomposites of separate reaction curves each having peaks (correspondingto primarily silicate and aluminate dissolution and precipitationreactions in the cement during hydration). Thus, the actual onset, orinitiation of, the renewed C₃A dissolution reaction that happens whenthere is no more available sulfate in hydrating cement, overlaps withthe C₃S reaction and vice versa. Thus, in line with typical methods inthe industry (including e.g., ASTM C563-17), the present inventors focuson the visible onset from the calorimetry curve. Further analysis, suchas taking first and second derivatives of the heat flow can helpidentify a reproducible renewed C₃A onset, as one can see a localmaximum in the second derivative in FIG. 5C corresponding to the onsetof the renewed C₃A dissolution (noted by the “|”). In FIG. 5C, thealuminate (C₃A) onset (“|”) occurs after the maximum rate of heatreleased due to the C₃S (“X”). It should be noted that separating thesummations or composites of the reaction curves (e.g. the silicatereaction from the aluminate reaction) of the hydration curve is verydifficult, and often requires other very sophisticated test methods tobe run in parallel (see e.g. “Interaction of silicate and aluminatereaction in a synthetic cement system: Implications and the process ofalite hydration,” in Cement and Concrete Research 93 (2017) pp. 32-44 byBergold et al.)

The difference between the times at which these two events describedabove occur is referred in this present invention as the Delta, Δ (i.e.time at C₃A onset minus time at maximum C₃S rate of heat release). Inthese cases where the system is oversulfated, Delta (Δ) will be greaterthan zero. In FIG. 5D, the Delta is larger, and the shoulder or onset ofthe renewed C₃A dissolution is less pronounced. However, the localmaximum of the second derivative can still clearly identify the onset.In FIG. 5E, the shoulder is barely perceivable, and the local maximum ofthe second derivative may be considered on the same order of magnitudeas the noise in the system. Although the time at maximum C₃S rate ofheat release is clearly defined, in this case, a system required toidentify a Delta value may be programmed to assign an extremeoversulfated indicator instead of an actual Delta since the onset of therenewed C₃A reaction is not clearly identifiable (through means such asa determining the local maximum of the second derivative). If the Deltais adjusted towards zero (becoming smaller), eventually, a local maximumof the second derivative will become clear, and the system can switchover to predict a numerical value for Delta when the second derivativeclearly provides an indication of the renewed C₃A onset. It should alsobe noted how clearly the maximum C₃S rate of heat release is identifiedin over-sulfated systems (e.g. FIGS. 5C-5E).

In some cases, when the cement does not have sufficient sulfate forcontrolling the renewed C₃A reaction, the C₃A dissolution will completebefore the peak of the silicate reaction. In this case, there is norenewed reaction after the peak in the C₃S rate of reaction leading to avisible onset. However, there is a visible shoulder that is due to thecompletion of the C₃A reaction. This shoulder will appear earlier intime with respect to the C₃S peak. This is illustrated in FIGS. 5B and5A. In FIG. 5B, again, the maximum C₃S rate of heat release isdesignated at “X”, while the shoulder is designated at “|”. Strictlyspeaking, the shoulder here is actually the visible change in curvatureof the curve corresponding to the completion of the aluminate reaction,that is, the point at which the dissolution of the C₃A is substantiallycomplete. After the completion of the aluminate reaction, the heat flowis primarily due to the silicate reaction. For simplicity, this feature(shoulder or visible change in curvature) is still called the onset. Aswas shown in FIGS. 5C and 5D, the onset is still clearly indicated by alocal maximum in the second derivative in FIG. 5B. In a consistentmanner, the Delta (Δ) is determined by subtraction the time of maximumC₃S rate of heat release from the “onset”. In these cases, the Deltawill be less than zero. If the system is mildly undersulfated, the C₃Ais allowed to react in an uncontrolled manner, and begins to hinder theC₃S reaction (see FIG. 5A). In this case, the global peak corresponds toa combined heat signal from both the C₃S and C₃A. Thus, this global peakis not strictly the C₃S peak, and cannot be used as such. In this case,the C₃S peak can be estimated from proper sulfated systems withnominally the same clinker. An undersulfated system is demonstrated inFIG. 5A, where there is no clear shoulder or sharp peak in the curve,and the second derivative shows no major local maximum. Similar to theextreme over-sulfated condition, a system required to return a Deltavalue may be programmed to recognize these conditions and assign anundersulfated indicator instead of a numerical Delta value. As the Deltais adjusted towards zero (becomes larger), eventually, a local maximumof the second derivative will become clear, and the system can switchover to predict a numerical value for Delta when the second derivativeclearly provides an indication of the “onset”.

The preceding paragraphs demonstrate one method to determine the Deltavalues. Other methods exist such as those outlined in ASTM C563-17, ASTMC1679-17, and in “Moving towards Automation” published in World Cement(July 2017).

In FIG. 6A, 1 day compressive strength (measured in megapascals) ismeasured for three different cements (A, B and C) as a function ofcumulative exothermic (heat output) over a 24-hour period (Joules/gramof cement). Furthermore, a square around the data points indicates themaximum strength for the given cement. Although within a given cement,the heat output correlates generally with the strength, the maximumstrength occurs at a different heat output for each cement.

However, as shown in FIG. 6B, when one day compressive strength values(megapascals) were measured for three different cements and plotted on agraph as a function of Delta (Δ), what appears to be a cogent patterncan be observed. In other words, the maximum strength of a cement isattained when its Δ value is in the range of (−)1 hours to (+)4 hours;more preferably, when its Δ value is (−)0 hours to (+)3 hours; and, mostpreferably, when its Δ value is 0.5-2.5 hours.

Based on the above discussion, a more complete and precise definition ofthe term Delta can be presented. A used herein, the term “Delta” (Δ)refers to the time lapse (e.g., hours) between the exothermic peakcorresponding to the silicate reaction (C₃S) and the visible onset ofthe exothermic peak which corresponds to (or approximates occurrence of)the renewed tricalcium aluminate reaction (C₃A) during hydration of thecement for systems that are oversulfated. In systems that areundersulfated, “Delta” (Δ) refers to the time lapse (e.g., hours)between the exothermic peak corresponding to the silicate reaction (C₃S)and the visible change in curvature corresponding to the completion ofthe tricalcium aluminate reaction (C₃A).

Although the relationship discussed above between strength and sulfatecontent was first explained by Lerch in 1946 (“The influence of gypsumon the hydration and properties of Portland cement pastes”, Proceedings,Vol. 46 of the American Society of Testing Materials), and is reflectedin various standards including ASTM C563-17, the complexity of thecement production process severely limits the ability to controlstrength consistently. More recent means have been proposed to use theDelta as an ongoing quality control method, whereby the Delta found incalorimetry curves at the sulfate level giving the maximum heat outputover the desired control period, for instance one day, three days etc.,is used as a control target. However, as it takes a significant time,typically 8-24 hours for the hydration to progress to the point theDelta can be calculated, this must result at best in sulfate beingadjusted to the conditions of 8-12 hours ago, not to the present time.Furthermore, this optimum Delta may have been established months ago, onpotentially very different clinker, so the logic that controlling to apast optimum Delta is limiting the utility of such an approach. In thepresent invention, the Delta is determined continuously, and the optimumDelta target can be continuously refined by inclusion of recent testdata in the model and even predicted in real-time. The present inventorstherefore believe that frequent and continual monitoring of both theDelta and optimum Delta can best be performed using infrared radiation(IR).

As used herein, the term “infrared” refers to light or radiation energyhaving wavelength(s) in the range of 750 nanometers (nm) to 1000micrometer (μm). The infrared (IR) radiation is commonly divided intothree regions: the near IR (0.8-2.5 μm), mid IR (2.5-25 μm) and far IR(25-1000 μm) wavelengths. Infrared (IR) waves interact with a molecule,based upon vibrational changes of the atoms within the molecule. Aportion of the radiation is absorbed, while the other portion isreflected radiation which can be sensed using an IR sensor and can bemonitored. The IR spectrum reflected is a unique property of eachmolecule. The IR spectrum can serve as fingerprint to identify thepresence and/or concentration of a molecule in a compound or materialsample, including mixtures of ground particles as in the presentinvention. It is believed by the present inventors that, while mid IRhas been used for organic compounds, the use of near IR (“NIR”), havinghigher frequency, can provide a greater resolution of information.

The use of IR sensors for assessing the content or quality of cement,clinker, and other powder materials, and for changing processingconditions, based on the spectral reflection is well-known. For example,in GB 2 111 193 A (1983), Ironmonger taught that IR could be used forirradiating a bed of clinker transported on a conveyor belt, and, basedon the color reflection, could be used for determining whether thematerial had sufficient calcium oxide content. By using a comparatorcircuit to compare signal output with a threshold value, Ironmongertaught that the output stage could be used essentially to provide acontrol signal whereby corrective action would automatically be taken ifthe detection signal were to rise above the threshold. See e.g., GB 2111 193 A at page 2, lines 54-59. As another example, in US Publ. No.2003/0015663, Mikula et al. explained that certain peaks of intensity ofreflected infrared (NIR) correlated with degrees of oxidation in oilsand ore; and they proposed on-line monitoring as a means fordetermining the degree of oxidation so that the information could beused to adjust processing conditions automatically (See e.g., US2003/015663 at paragraphs 0002-0009). In Publ. No. 2004/0021077 A1,Ambuel commented that NIR analyzers were used for decades to measureconstituents in pharmaceutical, refining, chemical manufacturing, andmedical diagnostic fields, and thus models could be used based on thespectra to predict individual components and content. In his U.S. Pat.Nos. 7,310,581; 7,663,108; and 7,924,414; Mound confirmed that IRspectroscopic analysis could be used for analyzing bulk materials, andin U.S. Pat. No. 7,924,414 he specifically noted that IR analyzers couldbe used for analyzing “the mixture of clinker and gypsum transported toa mill (160), and the cement composition transported to silos forstorage (175)” (See U.S. Pat. No. 7,024,414 at column 11, lines 49-56).

Data based on near infrared red (NIR), for example, has beensuccessfully correlated with concentrations of various chemical species,and this has been used is the study of cement systems. For example, inU.S. Pat. No. 5,475,220, correlations between cement phases (e.g. C₃S,C₃A) and NIR spectra are demonstrated. Similar results can be found inU.S. Pat. No. 8,887,806. These types of correlations are practiced today(see e.g., http://www.spectraflow-analytics.com/products.html). Althoughchemical species are predicted today, correlations to performancecharacteristics such as strength and Delta (Δ) have not been discovereduntil the present invention. Furthermore, prior art such as U.S. Pat.No. 7,924,414 focus on the raw materials entering the kiln, andsubsequent process changes concerning the kiln (see e.g., Column 10,Line 66 through Column 11, Line 16).

Hence, the present inventors believe that by using a suitable energysource (e.g., infrared emitter) to irradiate ground particles of cementas they exit the grinding mill, and measuring the reflected IR radiationusing an IR sensor, one may obtain information about the sulfate typeand level in the ground particles. One can also obtain predicted valuesfor actual performance properties corresponding to cement/sulfateparticles having the same or similar IR data profile. For example, thereflected IR data collected by the sensor can be compared using acomputer processor which is programmed to access database memory whereinIR data of previous ground clinker and calcium sulfate materials arestored along with (known or assigned) properties of the materials.

The invention is illustrated by the following enumerated exampleembodiments, including various exemplary aspects within the enumeratedexample embodiments. The following paragraphs describe a method formanufacturing cement; and, although “method” is ostensibly the term usedfor framing various process steps, it should be understood that theexample embodiments, and various aspect descriptions, which follow alsodescribe a “system” in that a computer processor electrically orelectronically communicative with various sensors can be configured orprogrammed to perform the variously described steps, as follows.

In a first example embodiment, the present invention provides a methodfor manufacturing cement, comprising:

(A) introducing, into a grinding mill, raw materials comprising clinker,a source of sulfate chosen from gypsum, plaster, calcium anhydrite, or amixture thereof, and optionally one or more supplemental cementitiousmaterials; grinding the raw materials, optionally with one or morecement additives, and optionally with water, to produce a ground blendof particles comprising ground clinker and calcium sulfate; andseparating the ground blend of particles within a classifier whereby afirst portion of the particles or the finished cement are sent to a siloor other receptacle for containing the finished cement and whereby asecond portion of the particles is recirculated into the grinding millfor further grinding;

(B) providing at least at least one sensor system chosen from infraredsensor system, laser diffraction sensor system, or both, and detectingemanation, reflectance, transmittance, or absorption of energy by orthrough the ground blend of particles or finished cement provided instep (A), and generating output signals corresponding to the detectedenergy;

(C) comparing output signals generated in step (B) to data stored inprocessor-accessible memory, the stored data comprising output signalvalues previously obtained from sensors measuring the emanation,reflectance, transmittance, or absorption of energy in the infraredspectrum, laser diffraction spectrum, or in both the infrared and laserdiffraction spectrums (the stored data being correlated with a physicalor chemical property of the corresponding finished cement, hydratedcement or cementitious product made with the cement, e.g., (i) strengthtest data, (ii) exothermic data; (iii) set initiation data; (iv) slumpdata; (v) dimensional stability data; (vi) air content data; (vii)prehydration level data; (viii) reduction or burn conditions data; (ix)cement particle size distribution data; or (x) a combination thereof);and

(D) in response to the comparison in step (C), adjusting (i) amount,form or both amount and form of calcium sulfate introduced into thegrinding mill in step (A); (ii) classifier settings, thereby to changerelative amounts of ground particles being sent to the silo and beingrecirculated back into the grinding mill; (iii) amount, type, or bothamount and type of cement additives introduced into the grinding mill;(iv) amount of water being introduced into the grinding mill; (v) theamount of air provided by adjusting power or speed of a fan or blowerconnected to ventilate the mill; (vi) amount or type of supplementalcementitious material introduced into the grinding mill; (vii) cementcooler setting, thereby to change the temperature of the finished cementor (viii) combination of any of the foregoing (e.g., in order to modifya physical or chemical property of the finished cement).

In a first aspect of the first example embodiment, step (B) comprisesirradiating the ground blend of particles or finished cement obtainedfrom step (A) using an infrared and/or laser radiation source. Morepreferably, the radiation comprises electromagnetic radiation havingwavelengths in the range of 300 to 1,000,000 nanometers (nm). Inpreferred example embodiments, the sensors are part of an integratedsystem wherein an emitter or radiation unit is combined with a sensor.

In a second aspect of the first example embodiment, the grinding millmay be chosen from a ball mill or roller mill, such as a vertical rollermill. The term “roller mill” includes vertical roller mills (“VRMs”) aswell as horizontal roller mills (e.g., Horomill® brand horizontal rollermills), as well as mills that crush particles into finer size throughnipped opposed rollers. VRMs have rollers which are pneumaticallycontrolled to rotate in vertical direction upon a circular rotatingtable, and have a classifier that is integrated into or part of the samehousing which contains the rollers and revolving table; and particlesare fed into the center of table and move towards the outercircumference of the revolving table and crushed under the path of therollers which are actuated by pneumatically assisted armatures. In VRMs,for example, at least one IR sensor is preferably located at the exit ofparticles from the housing which encloses the roller and classifiermechanisms, or, alternatively, along the pathway or conduit to thestorage silo.

In a third aspect of the first example embodiment, the method comprisesusing the at least one sensor system to detect the infrared (IR) (e.g.,energy having wavelengths in the range of 700 to 1,000,000 nanometers(nm) based upon IR reflected by, transmitted through, or absorbed by theground blend of particles or finished cement. (Note: 700 to 1,000,000 nmwavelength corresponds to frequencies of 430 THz to 300 GHz). The atleast one sensor system will preferably have ability to detect infraredradiation wavelengths in the range of 700 nm to 8 μm (430 THz to 37THz); more preferably, in the range of 700 nm to 3 μm (430 THz to 100THz); and, most preferably, 700 nm to 1400 nm (430 THz to 214 THz). NIR(Near Infrared Radiation) is typically 750-1400 nm (400-214 THz). SWIR(Short Wavelength IR) is typically considered to be in the range of1400-3000 nm (214-100 THz). MWIR (Mid-Wavelength IR) is typicallyconsidered to be in the range of 3-8 μm (100-37 THz). LWIR(Long-Wavelength IR) is typically considered to be in the range of 8-15μm (37-20 THz). FIR (Far IR) is typically 15-1000 μm (20-0.3 THz). ISO20473 specifies that NIR encompasses the range of 0.78-3 μm, MIR(mid-infrared) encompasses the range of 3-50 μm, and FIR (far-infrared)encompasses the range of 50-1000 μm.

More preferably, the at least one sensor system provides output signalscorresponding to the reflectance of energy by or through the groundblend of particles or finished cement. Using a sensor to measurereflectance (i.e., scattered reflection from the bed of particles) ofenergy from the IR source is preferred to measuring transmitted orabsorbed energy. In still further exemplary embodiments, the sensorsystem may provide output signals corresponding to discrete wavelengthranges, regions, or specified spectra. One may employ two or more IRsensors, each dedicated to a region within the IR range.

In a fourth aspect of the first example embodiment, the inventionprovides a method involving use of the at least one sensor system whichcomprises a source of radiation wavelengths in the range of 300-700 nmemitted by a laser, and obtaining data based upon scattering of thisradiation by and/or through the irradiated ground blend of particles orfinished cement. Two types of lasers are commonly used for particle sizeanalysis. First are red lasers, which typically are generated by HeNelasers, producing red light at 632.8 nm. Laser diodes are alsoavailable, which use GaInP or AlGaInP quantum wells. The second type oflasers are blue lasers for wavelength detection in the range of 360 nmto 480 nm. Helium-cadmium gas lasers produce blue light at 441.6 nm,while argon-ion lasers can produce blue light having wavelengths in therange of 458 nm to 488 nm. Diode lasers (445 nm) are becoming morepopular due to price. Semiconductor lasers, such as gallium nitride(GaN) can produce blue light as well. Many advances are occurring thisarea with new Thulium-doped and praseodymium-doped up-conversion lasers.

In a second example embodiment, which may be based upon the firstexample embodiment above, the invention provides a method wherein steps(A) through (D) are performed and repeated on at least a monthly basisor at shorter time intervals.

In other words, in a first aspect of this second example embodiment, themethod more preferably involves steps (A) through (D) being performedand repeated on at least a weekly, daily, once-per-shift, or even hourlybasis. Most preferably, the interval is every 15 minutes, and evensmaller intervals such as every 2-5 minutes.

In a third example embodiment, which may be based upon any of the firstthrough second example embodiments above, the invention provides amethod wherein steps (A) through (D) are performed and repeated forsuccessive 100,000 metric tons (MT) of cement clinker being ground inthe grinding mill. More preferably, the steps can be repeated at morefrequent intervals (e.g. 10,000, 1,000, or even smaller intervals).

In other words, in a first aspect of this third example embodiment, themethod involves steps (A) through (D) being performed and repeated forsuccessive 10,000 metric tons (MT), more preferably every 1,000 MT, evenmore preferably for successive 100 MT, and most preferably forsuccessive 10 MT of cement produced.

In a fourth example embodiment, which may be based upon any of the firstthrough third example embodiments above, the invention provides a methodwherein steps (A) through (D) are performed and repeated upon a detectedchange in the cement production process. For example, the detectedchange can involve a fuel change, a material input change (e.g.,composition of clinker, limestone, cement additives), water spray levelor spray rate, temperature, internal or external air temperature, etc.).

In a first aspect of the fourth example embodiment, steps (A) through(D) are performed and repeated upon a change in the production processcorresponds to a change in the kiln fuel feed rate or fuel type. It isknown that the type of fuel used to heat the kiln can have a majorimpact on the aluminate-sulfate balance of the clinker. Examples of fueltypes are coal, petcoke, oil, natural gas, as well as alternative fuelssuch as municipal waste, industrial waste (e.g. waste oil, animal feed,used carpets, used tires, etc.). Each of these fuels have differentsulfur contents. Furthermore, within a given fuel, for example, formunicipal waste, the sulfur can vary overtime. Therefore, changes infuel can cause issues for the cement producer as the resulting changesin the clinker need to be accounted for. Automatically detecting thechange in the sulfate-aluminate balance (and making the necessaryadjustments) not only enables a more consistent product through the fueltype change, but also can enable more fuel changes without performanceissues. In particular, the switching from a high sulfur containing fuelto a lower sulfur containing fuel can have an especially dangerousimpact on sulfate-aluminate balance, as it can cause formation of morehighly reactive orthorhombic C₃A. Using the present invention, thesesituations can be overcome to balance the sulfate correctly for eachfuel. This can be very beneficial for the environment as highly variablefuel sources such as waste (e.g. municipal waste), and can be used. Thepresent invention thus allows for more variable fuel sources to be used.

Moreover, the NIR system can be used to determine variations inpertinent cement chemical components (e.g. sulfates, calcium aluminateform), and this can help to select the optimum type and proportions ofdifferent fuels to maintain a balanced sulfate-aluminate system. Forinstance if high variation in orthorhombic to cubic C₃A ratio isdetected by the NIR system, waste fuel streams can be adjusted tomaintain consistent alkali to sulfate balance. Further, if environmentalconstraints dictate fuel blend changes on such a basis that the properalkali sulfate balance is difficult to achieve, and the NIR systemdetects such issues, then compensating kiln feed composition changes canbe made. As another example, if fuels used cannot supply enough sulfurto balance the alkali levels inherent in the raw materials, gypsum maybe added to the raw feed to supply the needed available sulfate. Thesepossibilities have previously been understood, but the NIR system'sability to continuously monitor composition is essential in enabling thedetermination of the level of variance and thus the relative importanceof taking such steps. As orthorhombic C₃A formation is also influencedby reducing conditions in the kiln, variation in the ratio absentsulfate-alkali balance changes in the kiln feed and fuel can beindicative of burning issues, which can then be addressed.

In a second aspect of the fourth example embodiment, the inventionprovides a method wherein steps (A) through (D) of the first exampleembodiment are performed and repeated when a compositional or chemicalchange in the raw materials, the raw meal, clinker, the finished cementor combination thereof, exceeds a predefined threshold. In particular,if C₃A orthorhombic content within the clinker (as measured or estimatedfrom, for example by XRD, XRF, etc.) exceeds a predefined threshold,steps (A) through (D) can be executed.

In a third aspect of the fourth example embodiment, the inventionprovides a method wherein steps (A) through (D) of the first exampleembodiment are performed and repeated when a change in the cementfineness exceeds a predefined threshold, such as a maximum deviationvalue (fineness target or range). This fineness characteristic can bemeasured offline (e.g. with a manual Blaine measurement) or online (e.g.with a particle size analyzer).

In a fourth aspect of the fourth example embodiment, the inventionprovides a method wherein steps (A) through (D) of the first exampleembodiment are performed and repeated when a change in a kiln process, amill process or a both occurs. For example, if the flame length changeswithin the kiln, steps (A) through (D) can be executed. As anotherexample, if the water spray rate within the mill is changed, steps (A)through (D) can be executed.

In a fifth example embodiment, which may be based upon any of the firstthrough fourth example embodiments above, the processor is programmed toadjust the sulfate entering the mill in terms of calcium sulfate type,feed rate, or both type and feed rate. For example, this may beaccomplished by adjusting feed rate of a calcium sulfate source into themill or the ratio between forms of sulfate. As another example, duringintroduction of sulfate materials into the mill, one may add acombination of gypsum and anhydrite into the mill; and, once these arein the mill, one may adjust the temperature and moisture within the millto control the dehydration of gypsum to plaster.

In a first aspect of the fifth example embodiment, the source of calciumsulfate introduced into the mill in step (A), whether in the form ofgypsum, plaster, or anhydrite, can include synthetic versions (e.g.,synthetic gypsum), phosphogypsum, as well as natural forms (e.g.,natural anhydrites). Sulfates can include alkali or alkaline earthsulfates (e.g., calcium sulfate, sodium sulfate, potassium sulfate).

In a second aspect of the fifth example embodiment, the ratio betweendifferent forms of sulfate entering the mill is determined by using asensor that monitors the sulfate source entering the mill. For example,an NIR sensor can be programmed to detect the relative amounts of gypsumand anhydrite (as plaster is rarely added into the mill, but appears asthe gypsum is dehydrated once inside the mill) within the sulfate sourcebeing introduced into the mill. The processor can be programmed to usethis information to adjust the total sulfate feed rate, adjustindividual rates of gypsum and/or adjust mill processes that can controlthe ratios between the different sulfate forms after being introducedinto the mill (including the gypsum to plaster ratio).

Both the amount and form of sulfate can affect characteristics of acement, such as its strength and Delta. Thus, in a third aspect of thefifth example embodiment, the exemplary method further comprises storingdata regarding total and relative amounts of the different sulfate formsentering the mill, and this can be performed during steps (A) through(C), and the data can be stored into processor-accessible memory (e.g.,for use as later reference values). By combining the sulfate informationas well as performance predictions generated from step (C),relationships between the sulfate adjustments and performancecharacteristics can be developed and used to make more efficientadjustments to the cement production process.

In a sixth example embodiment, which may be based upon any of the firstthrough fifth example embodiments above, the processor can be programmedto adjust supplementary cementitious materials (SCM) entering the mill(e.g., being introduced into the mill at step (A)) in terms of type orfeed rate, or both type and feed rate. This may be done for example byadjusting feed rate of an SCM source into the mill, the ratio differenttypes of SCM introduced into the mill, or the respective feed rate ofdifferent SCM sources into the mill. For example, if a prediction basedon the NIR, LD, T and/or M/RH sensors indicate that the strength (e.g.1, 28 day) of the finished cement is 10% higher than a pre-definedstrength target, the amount of fly ash can be adjusted until thepredicted strength of the finished cement (including the adjustedproportion of fly ash) is reduced to the target. A similar approach canbe taken if the predicted strength is lower than the target.

In a first aspect of the sixth example embodiment, the source ofsupplementary cementitious materials (SCMs) introduced into the mill instep (A) is chosen from limestone, fly ash, granulated blast furnaceslag, clay, calcined clay, natural pozzolan, or a mixture thereof.

In a second aspect of the sixth example embodiment, the chemicalcomposition of SCMs entering the mill can be monitored using one or moresensors to measure SCM entering the mill. For example, an NIR sensor canbe programmed to detect the additional source of aluminates within theSCMs that must be accounted for in order to accurately adjust thesulfate-aluminate balance, which can affect the strength of the cement.SCMs may also have a more negative impact on early strength developmentdue to higher amorphous contents and thus deserve monitoring andconsideration in the comparison and adjustment steps.

In a third aspect of the sixth example embodiment, the exemplary methodfurther comprises storing information regarding composition of the SCMin a processor-accessible database during performance of steps (A)through (C), and the data can be stored into processor-accessible memory(e.g., for use as later reference values). By combining the compositioncharacteristic (e.g. C₃A content, amorphous content) information as wellas performance predictions generated from step (C) of the first exampleembodiment, and relationships between the SCM adjustments andperformance characteristics can be developed and used to make moreefficient adjustments to the cement production process.

In a seventh example embodiment, which may be based upon any of thefirst through sixth example embodiments above, the processor isprogrammed to adjust the introduction of chemical additives into thegrinding mill in terms of type, formulation, amounts, dosage rate, or acombination thereof. For example, the dosage rate of a particularchemical or group of chemicals may be adjusted. The relative amounts ofchemicals used in a formulation may be adjusted. As a further example,the processor can be programmed to adjust the rate by which specificchemical additives are introduced into the grinding mill.

The cement additive can be a conventional grinding enhancement additive,a strength enhancing additive, or other agent, or combination thereof,that modifies one or more properties of the cement during grinding, ofthe cement during hydration, or of the cement material after it ishardened into concrete, mortar, masonry, or a structure. The cementadditive amount can be adjusted based on a strength prediction or otherperformance parameters, such as Delta, total heat released over aspecified period of time (e.g. 24 hours), set time, slump, dimensionalstability, prehydration level, etc. For example, if a prediction basedon the NIR, LD, T and/or M/RH sensors indicates that the strength of thefinished cement is 10% lower than a pre-defined target strength for agiven age (e.g. 1 day or 28 days), the amount of a strength enhancingcement additive can be adjusted until the predicted strength of thefinished cement (including adjusting proportion of cement additive) isincreased to the target. If the predicted strength is higher than thetarget, the classifier setting can be adjusted to decrease the Blainespecific surface area in order to reduce the mill energy consumption,thus providing an energy and cost savings. Adjustment of chemicaladditive dosage can also cause a change in temperature due to the changein grinding efficiency. Using a combination of adjustments to both thesulfate feed and mill conditions, a wide variety of absolute amounts ofgypsum/plaster/anhydrite can be achieved.

In a first aspect of this seventh example embodiment, the cementadditive may be a conventional alkanolamine or acetic acid (includingany salt or derivative thereof. For example, this may includetriethanolamine (“TEA”), acetic acid, triisopropanolamine (“TIPA”),diethanolisopropanolamine (“DEIPA”), ethanoldipropanolamine (“EDIPA”),tetrahydroxyethylethylene diamine (“THEED”), methyl-diethanolamine(“MDEA”), ethanol diglycine (“EDG”), a glycol, a glycerol, and mixturesthereof. Other conventional additives may be employed as desired bythose skilled in the art.

In a second aspect of this seventh example embodiment, the cementadditive may be chosen from the group of set accelerators and strengthenhancers comprised of chloride, bromide, thiocyanate, iodide,perchlorate, formate, thiosulfate, nitrate and nitrite alkali or earthalkali salts (such as sodium sulfate), and mixtures thereof.

In a third aspect of this seventh example embodiment, the cementadditive may be chosen from the group of set retarders comprised ofgluconate salt, gluconic acid, molasses, sucrose, or corn syrup, ormixtures thereof.

In a fourth aspect In a third aspect of this seventh example embodiment,the cement additive may be chosen from defoamers comprising of (i)ethoxylated, propoxylated fatty alcohol or alkylphenol, (ii)polyalkoxylated polyalkylene polyamine, or (iii) a mixture thereof.

In a fifth aspect of this seventh example embodiment, the cementadditive may be a combination of the above cement additives thatprovides performance enhancement to the ground cement. For example,organic acid chemicals such as tartaric or citric acid may be added tocontrol the C₃A side of the sulfate balance to complement a sulfateadjustment if needed (e.g. in situations where no more sulfate can beadded because of limitations imparted by ASTM C1038/C1038M-14b).

In a sixth aspect of this seventh example embodiment at least onecompositional or categorical characteristic of the chemical additive isstored in a processor-accessible database during performance of steps(A) through (C), and the data can be stored into processor-accessiblememory (e.g., for use as later reference values). Compositionalcharacteristics may include, for example, the relative amounts ofcertain chemicals within the chemical additive (e.g. amine, defoamer,etc.). A categorical characteristic can simply be the identificationlabel for the given additive. By combining this information as well asperformance predictions generated from step (C), relationships betweenthe adjustments and performance characteristics can be developed andused to make more efficient adjustments to the cement productionprocess. In other words, the formulation of the additive can be adjustedin real time based on how efficient the additive formulation is inadjusting one or more performance characteristics.

In an eighth example embodiment, which may be based upon any of thefirst through seventh example embodiments above, the processor isprogrammed to adjust a kiln process, a mill process or both.

In a first aspect of this eighth example embodiment, the processor isprogrammed to adjust the operation of the classifier that is used forremoving sufficiently fine particles to send them to the storage siloand to recirculate coarser particles back into the mill. For example,the classifier can be adjusted to select out finer or coarser particles.The classifier can be adjusted a number of ways to change the particlesize distribution and/or specific surface area of the finished cement,including air speed within the classifier, the rotational speed ofdistribution plates, vane settings, loading rates, and other factors.Many performance aspects of cement are affected by the particle sizedistribution and/or specific surface area, including strength, set time,workability, etc. By performing adjustments to the classifier, theseperformance characteristics can be adjusted. The classifier can also beadjusted in response to other changes in the mill process, such as tothe introduction of a grinding aid. Because grinding aids can increasethe efficiency of the grinding and classification process, theclassifier can be adjusted to take into account the efficienciesimparted by the grinding aids to realize potential energy and costsavings.

In a second aspect of this eighth example embodiment, the processor canbe programmed to adjust the operation of the water spray rate within themill. One way to adjust the sulfate source is to control the temperatureand humidity within the mill and thus the dehydration of gypsum toplaster (and furthermore to anhydrite in some cases), i.e. the ratiobetween the sulfate forms (gypsum/plaster/anhydrite). Temperature andhumidity can be adjusted through the control of the mill water andtemperature systems. Using predictive models, or real-time feedback fromsensors (e.g. temperature, moisture or relative humidity sensors), theprocessor can be programmed to adjust water spray rate to adjust thetemperature and humidity and thus the rate or amount dehydration ofgypsum to plaster. Minimizing water spray helps to avoid or to minimizeprehydration of the cement.

In a third aspect of this eighth example embodiment, the processor canbe programmed to adjust the amount of air provided to ventilate the millby adjusting the power or speed of a fan or blower connected to themill. In addition to the water spray, the fan pulling air through themill can also control the temperature (and thus the forms of sulfate).Again, a predictive model or real-time feedback from sensors can be usedto determine deviations from pre-defined targets and thus whatadjustments need to be made to incur a change of thegypsum/plaster/anhydrite forms.

In a fourth aspect of this eighth example embodiment at least oneprocess parameter of the kiln or mill is stored in aprocessor-accessible database during performance of steps (A) through(C), and the data can be stored into processor-accessible memory (e.g.,for use as later reference values). Process parameters may comprise, forexample, the water spray rate, the air speed, a flame size, a fuel rate,an elevator bucket speed, etc. By combining this information as well asperformance predictions generated from step (C), relationships betweenthe process adjustments and performance characteristics can be developedand used to make more efficient adjustments to the cement productionprocess.

The processor for purposes of step (D) can be programmed to performadjustments to achieve a variety of changes to the cement productionsystem to improve the quality of the cement. For example, the sulfateamount, the SCM blend, and any cement additive(s) can be optimized, interms of amounts and in real time, to produce a target or maximumstrength at 1 day (or other “ages” such as 28 days). As another example,the amount of water spray, air flow, and temperature can also beoptimized for maximizing strength. Any of these factors or combinationof these factors can alternatively be optimized for a target set time,or for compatibility with a particular concrete admixture. Anotherpossibility is optimizing the sulfate-aluminate balance for a givenclimate (e.g. hot climates require more sulfate). Aside fromoptimization, characteristics such as strength can be optimized forconsistency. That is to say, for example, the sulfate may be optimizedfor the given clinker, but the strength can be reduced (or increased) tomatch a target strength by, for example, adjusting the fineness of thecement (which depends on a control loop involving a particle sizeprediction from, for example a laser diffraction sensor system, or NIRsensor system) and/or by adjusting the type or amount of cementadditive.

The choice of which adjustment(s) to make can be prioritized based onseveral factors. Some cement plants may be able to adjust only some ofthe processes described in (i) to (vii) of step (C) of the first exampleembodiment above. For example, blended cements (clinker with SCMs) arenot common in the USA, and require additional feed systems. However, inEurope, blended cements are typical. The adjustments may also beprioritized based on their relative effect upon performance. Forexample, as fineness has a major impact on the strength of the cement(especially at early ages), it may be one of the first processes toadjust (such as by adjusting the separator settings and/or adjusting thedosage of the grinding aid). However, if prioritizing is based onmanufacturing cost, it may be more preferred to grind coarser particlesand instead add or adjust strength enhancing cement additives, decreasethe amount of SCMs or adjust the sulfate balance. In another scenario,the CO₂ emissions may be a priority, and in this case, the amount ofSCMs may be increased, which may require adjustments to the fineness,cement additive content or sulfate balance. Prioritization also dependson the sensor systems employed. Using an NIR sensor system with a laserdiffraction sensor system may allow the cement plant to measure andmanage the sulfate balance, and at the same time maintain the strengthat a constant value by measuring and managing the fineness as well asadding cement additives. The choice of adjustments can also depend onbalancing several different performance factors. For example, aparticular sulfate level may be ideal for achieving a certain targetstrength, but not so favorable for achieving an acceptable settingbehavior, or slump, slump retention as well as admixture response. Thepresent invention thus makes it now possible to have flexibility tomanage all of these different scenarios.

In a ninth example embodiment, which may be based upon any of the firstthrough eighth example embodiments above, the method further comprisingcollecting data from at least one non-IR, non-laser sensor disposed orlocated within, or at the inlet or outlet of: (i) the grinding mill,(ii) an air flow inlet, outlet, or channel connected to grinding mill,or (iii) a kiln that produces cement clinker material introduced intothe grinding mill. The data (e.g., output signal, associated value) fromthe at least one sensor is preferably stored and associated with dataand/or associated value(s) previously stored in processor-accessiblememory, for example, to serve as later reference values useful for step(C). The signal output of a sensor, or a value which is associated tothe signal output, or both, may be stored into memory as a history ofthe process event and can be used in step (C).

In a first aspect of the ninth example embodiment, data collected fromtemperature, moisture, relative humidity sensors, or combinationthereof, is stored in with the data stored in processor-accessiblememory, where it can be used later, e.g., such as for reference in thecomparison process described in step (C). Temperature and moisture data(which can be used to calculate relative humidity), thus producingfurther data or associated values which can be stored and used later asreference values in step (C)) can help determine dehydration states ofgypsum (to plaster) within the mill. Also, because IR signals (i.e.,NIR) are sensitive to temperature and moisture, use of independenttemperature and moisture sensors can help to correct or to eliminate theeffects of moisture which could otherwise adversely affect or complicateanalytical predictions of cement properties (e.g. Delta, strength) basedon the IR signals.

In a second aspect of the ninth example embodiment, the method of theinvention further comprises an X-ray diffraction (XRD) sensor, X-rayfluorescence (XRF) sensor, thermogravimetric (TGA) sensor, particle sizedistribution (PSD) analyzer, prompt gamma neutron activation (PGNAA)analysis, and further comprises obtaining data from at least one of theafore-mentioned sensors and storing the data in processor-accessiblememory for use in later reference, such as the previously stored datadescribed in step (C). XRD, XRF, TGA, PSD, or cross-belt analyzers suchas a PGNAA sensor from ThermoFisher® Scientific (of Waltham, Mass.) canbe used to provide chemical analysis on a continual basis, which canhelp to confirm, improve or update calibrations for IR predictions (e.g.Delta, strength). Such sensors can also be used to trigger any of steps(A) through (D). For example, if the raw meal composition changes asdetected by a PGNAA sensor, steps (A) through (D) of the first exampleembodiment is executed.

In a third aspect of the ninth example embodiment, exemplary methods ofthe invention further comprising using an ultrasonic sensor or otherrange-finder type sensor to generate data that can be stored inprocessor-accessible memory (e.g., step (C)). This information can beused, for example, to determine the distance from an IR sensor to themeasured particles as they are conveyed on a conveyor belt or within achute or other open channel. Using this distance information, the NIRreceived signal can be corrected in real-time for any changes in thedistance from the probe to the measured particles. As another example, aparticulate concentration sensor can be located in an air slide whereinthe particles are measured by the NIR sensor, and this particulateconcentration sensor can be used to correct in real-time for any changesin the concentration of the measured particle within the air-slide.

Furthermore, the processor in step (C) of the first example embodimentcan be programmed to take into account additional inputs or signalsregarding the cement manufacturing system, and these can be used to makethe comparison. For example, information about the raw feed (rawmaterial proportions, chemical composition), kiln processes (e.g.temperature, flame size, oxygen levels, output volume), fuel source andchemical composition, clinker size and chemical composition, millprocesses (temperature, water spray, ventilation, mill void fillingratio, size of steel balls used, ball loading (which can be tied toacoustic sensor levels)). In addition, categorical inputs such as thename of an SCM type or additive type can be used to help indicate whichdata tables to use when predicting performance. For example, strengthpredictions when using a TEA-containing cement additive may be differentthan when using a DEIPA-containing cement additive. The formulation namecan identify which predictive relationship to use.

In a tenth example embodiment, which may be based upon any of the firstthrough ninth example embodiments above, the method further comprisesproviding an IR or laser sensor within an elevator bucket, conveyorbelt, air slide, or pneumatic conveying device within or connected tothe grinding mill. Sensors for measuring reflected and/or absorbedradiation can be used on moving cement particles, or cement particlesamples which are removed from the production stream temporarily orpermanently for IR radiation testing. Removal of a sample can be done“manually” (when desired) or “automatically” (at programmed intervals).Hence, the sensors used in step (B) for monitoring reflected, absorbed,and/or transmitted IR radiation can be located within a manuallyoperated sampler or auto sampler.

In a first aspect of the tenth example embodiment, the method furthercomprises the use of an auto sampler, preferably such that if sufficientamount of sample can be removed from the product stream for IR testing,additional testing can be performed to measure strength, heat output,set time, workability, shrinkage or expansion, air content, prehydrationor clinker reduction, or burn conditions associated with the cement.

In a second aspect of the tenth example embodiment, a combination ofsensors at various locations can be employed. One preferredconfiguration involves location of a near infrared sensor (NIR), a laserdiffraction sensor (LD), a temperature sensor (T), and a moisture orrelative humidity sensor (M/RH) along or within a conduit, conveyerbelt, channel, or pipe through or along which finished cement isconveyed from the grinding mill to a silo or other storage container.Another preferred configuration is to have the NIR, LD, T and M/RHsensors located along or within a conduit, conveyer belt, channel, airslide, or pipe through which the recirculated particles are redirectedback into the grinding mill. Still another preferred configuration is tohave the T and M/RH within the grinding mill and the NIR and LD along orwithin a conduit, conveyer belt, channel, air slide or pipe through oralong which finished cement is conveyed from the grinding mill to a siloor other storage container.

In a third aspect of the tenth example embodiment, temperature sensorscan be mounted after the grinding mill to monitor finished cement beingsent to the cement silo (or other storage for the finished cement),including an additional temperature sensor in the silo itself. Inaddition, moisture or relative humidity sensors can also be mountedafter the grinding mill to monitor the cement being sent to the cementsilo.

In a fourth aspect of the tenth example embodiment, multiple sensors(NIR, LD, T, or M/RH) along a path (such as the path or conduit from themill to the storage silo; or even before, within and after the cementcooler) or at different vertical levels within the storage silo, may beused to enable the operator or processor-controlled monitoring system topredict or measure the amount of gypsum conversion to plaster due todehydration. This information can be used to adjust the source ofcalcium sulfate such that after conveyance to the cement silo, the finalproduct will have the desired amount and forms of calcium sulfate. Atemperature sensor (optionally in combination with a moisture sensor orrelative humidity sensor), for example, can also be used to predict theamount of dehydration of gypsum to plaster. In other words, adjustmentsof the sulfate form and content can also be aided by an additionalfeedback system where the temperature of the finished cement as it isconveyed to the silo is monitored until the temperature of the cementhas cooled to a final temperature (i.e. through temperature sensorsinstalled in the silo or in proximity of the cement cooler). Thisinformation can be useful, as cement exiting the mill can still be atelevated temperatures (e.g. over 100° C.), and gypsum can still bedehydrating to plaster. By measuring temperature of cement andgypsum/plaster upon exit from the mill or classifier, and by knowing thetemperature in the silo, the amount of dehydration can be predicted.This information can then be relayed to the processor which controlssulfate levels, so that adjustments can be made to take into accountdehydration in the cement after it leaves the mill. Alternatively, thecement cooler settings can be adjusted to prevent further dehydrationbased on the temperature measurements.

In a fifth aspect of the tenth example embodiment, the inventionprovides a method wherein at least two energy radiation/sensor systemsare employed, one of which is based on use of infrared sensor systemhaving an infrared radiation emitter and infrared radiation sensor, thesecond of which is based on use of a laser diffraction sensor systemhaving a laser emitter and radiation sensor for detecting laser energypassing through the irradiated finished cement. When two energyradiation/sensor systems are employed, two independent measurements canbe taken. These independent measurements can be used to perform avariety of different tasks, for example, one measurement can be used todetermine or improve the accuracy of the other measurement. Bothmeasurements can also be used in combination to help train algorithms(e.g. regressions or machine learning sets) to predict differentperformance values (e.g. strength, exothermic results such as Delta).Where possible, the two independent measurements can help to controldifferent parameters such as particle size (e.g. with the laserdiffraction measurement) and sulfate balance (e.g., with the NIRmeasurement as measured by the Delta value).

In a sixth aspect of the tenth example embodiment, the invention furthercomprises employing an NIR sensor to determine chemical composition ofthe clinker entering the grinding mill. This signal can be compared tosignals from the ground cement, which necessarily represents thecomposition of the bulk clinker, to better refine predictiverelationships. It is understood that the signals obtained from clinkermay be different compared to signals from crushed cement as the NIRreflectance of a clinker will mostly represent the surface. It is alsounderstood that relative proportions of the chemical components of thesurface of clinker may be different from the bulk of the clinker.

In an eleventh example embodiment, which may be based upon any of thefirst through tenth example embodiments above, the invention provides amethod wherein, in step (C), the stored data obtained from finished orhydrated cement, is chosen from (i) strength test data, (ii) exothermicdata; (iii) set initiation data; (iv) slump data; (v) dimensionalstability data; (vi) air content data; (vii) prehydration data; (viii)reduction or burn conditions data; (ix) cement fineness data; or (x) ora mixture thereof.

In a first aspect of this eleventh example embodiment, the stored datais based on strength data and is obtained by casting a compositioncomprising the irradiated finished cement and water, with optionalaggregates (either sand or gravel or both), and allowing the compositionto harden after a specified period of time (for example, 6 hours, 1 day,2 days, 3 days, 7 days, 28 days, 56 days, etc.). After the prescribedtime has elapsed, the material (frequently cast as a prism (including acube) or cylinder) is subjected to compressive load. The compressivestrength (which may be tested, for example, in accordance with ASTMC109/C109M-16a or EN-196-1:2016) is calculated upon failure of thespecimen. These tests are usually performed under specifiedenvironmental conditions (e.g. temperature, humidity specifications),but can be performed at different conditions based on where the cementwill be used by the cement producer's customers (e.g., if concreteproduced with the given cement is mostly cast in warm climates, thespecimens may be cast at temperatures elevated relative to what isspecified by e.g. ASTM C109/C109M-16a).

In second aspect of this eleventh example embodiment, the stored data isbased on calorimetric testing, whereby the heat released from a cementpaste (cement and water), mortar (cement paste with sand), or concrete(mortar with gravel) are recorded over time. Different types ofcalorimetric tests exist such as semi-adiabatic, and isothermal(semi-adiabatic systems allow heat to leave the system, while isothermalrefers to a system where the heat is measured at a constanttemperature). Many different methods exist to look at heat releasedduring the hydration of cementitious materials. For example, the totalheat released over a period of time (e.g. 24 hours) can be quantified,and has been correlated to strength for similar cements. Heat releaseddue to different reactions can also be quantified both in the intensityand time at which the reactions begin, are at their highest rate, orend.

In a third aspect of this eleventh example embodiment, the stored datais based on set initiation data, which typically involves initial setand final set times for a hydrated cement sample. The set times can bedetermined by penetration tests (or proctor tests), where thepenetration into the material is recorded over time, and initial andfinal set are determined when the penetration meets certain prescribedvalues. Values such as initial set can also be determined by other typesof tests, for example using shear wave reflection. Because liquids donot reflect shear waves, as the material hardens (sets), the shear wavereflection increases. Set time has also been shown to be indirectlyestimated from calorimetric testing data.

In a fourth aspect of this eleventh example embodiment, the stored datais based on slump data. Slump data is a simplified way to refer torheological behavior. The rheological data may be based upon or includedata which reflects yield stress, viscosity, thixotropy (as measured forexample by a rheometer, see e.g. ICAR rheometer), or more practicalmeasurements such as slump (which can be measured using the drop inheight when concrete is demolded from a truncated cone) or slump flow(which is usually measured in terms of horizontal spread of the concreteon a steel surface). In the cement plant environment, workability can bemeasured on cement pastes by, for example, the normal consistency test(see e.g. ASTM C187-16), or by use of flow tables with mortars (seee.g., ASTM C230/C230M-14). Hence, for example, reflected IR data may becorrelated with slump, slump flow, or other rheology measurements.

In a fifth aspect of this eleventh example embodiment, the stored datais based on dimensional stability data, which involves changes in volumeover time, such as shrinkage and expansion. There exist many standardmeasurements including ASTM C157/C157M-17 and ASTM C596-09(2017), forexample. Hence, for example, reflected IR data may be correlated withsuch standard measurements.

In a sixth aspect of this eleventh example embodiment, a cement additivedosage response to one or more of the stored data is determined. Thedosage response is calculated as the amount of cement additive requiredto achieve a given level of performance of a parameter such as strengthat a given time (e.g. 24 hours), and alternatively set time, shrinkage,particle size distribution and/or specific surface area or other cementresponse to cement additive may be used. Furthermore, cement additives,such as grinding aids can also affect other properties such as thethroughput of the mill. This data, usually represented as a responseover different dosages, can be created by testing a given performanceparameter for a range of dosages. Dosage responses can then be used toselect a dosage or cement additive to be used during the production ofthe cement. Alternatively, if a less than ideal dosage or cementadditive type is being used, instead of switching the dose or cementadditive, the production parameters (e.g. sulfate form or amount) can beadjusted to improve the dosage response. Further, if the sulfate form isless than ideal but cannot easily be altered, the cement additiveformulation can be changed based on knowledge of interaction of thecement additives with that sulfate form. Cement additives can, forexample, include quality improvers (which can improve strength or otherproperties), grinding aids, which can improve grinding efficiency, orboth.

In a seventh aspect of this eleventh example embodiment, a concretechemical admixture dosage response to one or more of the stored data isdetermined. The dosage response is calculated as the amount of admixturerequired to achieve a given performance such as strength, set time,shrinkage reduction or other performance response. Typical concreteadmixtures include “water reducing admixtures” (e.g., lignosulfonates,naphthalene sulfonates, polycarboxylate dispersant polymers), retarders,and other chemical admixtures that can affect the sulfate balance (andhence flash and false set) in many different ways. For a cement that isclose to being under-sulfated (and hence has the risk of to flash set,or in other cases extended set), the use of concrete admixtures may pushthe cement system further towards being under-sulfated. Thus, the cementplant may choose to optimize towards a higher Delta (i.e. a greateramount of sulfate) in order to prevent such problems (i.e. the Delta isoptimized for the presence of the concrete admixture). Thus, thepractical target Delta may be higher than the Delta at optimum strength,in order to accommodate known field condition demands.

In an eighth aspect of this eleventh example embodiment, the stored datais based on the content or volume of air entrapped or entrained within acementitious mixture, also known as the air content. There exist manystandard measurements including ASTM C185-15a for mortar or ASTMC173/173M-16 for concrete. Cement additives can have an effect on theair generated as measured using these test methods. Undesirable airgeneration can lead to lower strengths for concrete or mortar mixturescreated from the cement. Hence, for example, reflected IR data may becorrelated with such standard measurements.

In a ninth aspect of this eleventh example embodiment, the stored datais based on the prehydration level of the cement particles (whichindicates the amount of water chemically absorbed onto the surface ofthe cement particles). The prehydration level of the cement particlesmay be quantified using Thermogravimetric Analysis (TGA) and morespecifically using a methodology to calculate Wk as described in“Prehydration of cement: global survey and laboratory results,” in ZKG 6(2018) by Silva, D. et al). Other quantifications of prehydration levelsmay include the total weight loss of the material expressed in percentweight.

In a tenth aspect of this eleventh example embodiment, the stored datais based on reduction or burn conditions data of the cement particles.During production of cement, changes to the kiln process and theresultant clinker composition can lead to reduction, over burn, andunder burn conditions. Reducing ‘oxygen deficient’ kiln conditions canhave a significant detrimental effect on the clinker and the resultingcement performance in terms of strength, setting, flow workability andkiln performance (fuel costs and maintenance). Reduction causes a seriesof changes to the chemistry and mineralogy of an affected clinker,including a raised orthorhombic C₃A content, and reduced alitereactivity etc. The level of reduction in a specific clinker sample maybe quantified using a combination of methods. Firstly, by thedetermination of abnormal changes in the actual clinker mineralogydetermined by Quantitative X-ray Diffraction by Rietveld (known as QXRD,or alternatively XRD), as compared with the estimated qualitiescalculated from the bulk elemental composition—Bogue analysis (See e.g.,Bogue, “The Chemistry of Portland Cement,” Journal of PhysicalChemistry, Vol. 52 (Reynolds Publishing Corporation (New York N.Y.1947), which is determined by X-ray Diffraction analysis (XRF). Suchclinker reduction can also be quantified by optical microscopy which canconfirm the presence of atypical changes to the clinker microstructure(See e.g., Sibbick and Cheung, “Cement Clinker Microscopy as an Aid toDetermine Performance Differences in the Presence of Chemical Additives,36^(th) International Cement Microscopy Association Conference, Milan,Italy (2014)); and, finally, by the use of chemical reduction tests suchas the Magotteaux test (See e.g., Hardtl, R., “Magotteaux test forcement analysis, in Betonwerk+Fertigteil-Technik, Vol. 69 (2003), orManns, W., “Zur Braunverfärbung von Betonwaren—Möglichkeit derfrühzeitigen Erkennung,” Betonwerk+_Fertigteil-Technik, Vol. 68 (2002)).In a similar manner other cement kiln processes in terms of degree ofburning (over to under) and other factors (raw feed residual issues,combinability, and cooling etc.) can be determined primarily by opticalmicroscopy (alite crystal size, free lime and belite cluster contents,flux phase crystallinity) of the whole uncrushed clinker. However, thesemicrostructural and compositional differences can also be verified bycorresponding XRD and XRF analyses. Underburned clinker typicallyexhibits a less than optimum combination of the raw feed components intothe primary calcium silicate and calcium aluminate phases, leavingpartially burnt raw feed, undefined calcium silicate melt and higherthan optimum free lime components. Over-burned clinker typicallyexhibits high levels of combination into large well-formed andpotentially lower reactivity alite crystals (>60 microns in diameter)and correspondingly lower belite, free lime and flux phases which cannegatively impact late age strength development.

In an eleventh aspect of this eleventh example embodiment, the storeddata is based on particle size distribution data of the cementparticles, which involves size of a given set of particulate material.For example, the median or average particle size can be determined basedon the size distribution. Other values may be the mass fraction ofmaterial above or below a given size, e.g. −32 micron represents thefraction of material below 32 microns, or the specific surface area, asmeasured by the Blaine test or by a laser diffraction PSD method.Furthermore, characteristics of the Rosner-Ramler relationship, such asthe slope can also be used. Various particle size analysis instrumentsare commercially available.

In a twelfth example embodiment, which may be based upon any of thefirst through eleventh example embodiments above, the invention providesa method wherein, in step (B), the at least one sensor system is aninfrared sensor system having an infrared emitter to irradiate theground blend of particles or finished cement and an infrared sensor todetect infrared radiation reflected (IR) from the irradiated groundblend of particles or finished cement, the infrared sensor systemthereby obtaining reflected IR data; and, in step (C), the processorcompares the reflected IR data with stored reflected IR datacorresponding to strength test data of hydrated ground blend ofparticles or finished cement at a predetermined age.

In an thirteenth example embodiment, which may be based upon any of thefirst through twelfth example embodiments above, the invention providesa method wherein, in step (B), the at least one sensor system is aninfrared sensor system having an infrared emitter to irradiate theground blend of particles or finished cement and an infrared sensor todetect infrared radiation reflected (IR) from the irradiated groundblend of particles or finished cement, the infrared sensor systemthereby obtaining reflected IR data; and, in step (C), the processorcompares the reflected IR data with stored reflected IR datacorresponding to exothermic data stored in processor-accessible memory.The exothermic data is obtained by calorimetric measurement, over aperiod of time, of hydrating particle blends comprising ground clinkerand source of calcium sulfate, wherein (i) total heat output is stored;(ii) two different exothermic time values are stored, a first valuecorresponding to a time T₁ indicating when the maximum silicate reactionrate occurs after initiation of cement hydration, a second valuecorresponding to a time T₂ indicating the visible onset of when eitherthe renewed tricalcium aluminate reaction rate occurs (if occurringafter T₁), or when the completion of the aluminate reaction occurs (ifoccurring before T₁) after initiation of cement hydration; or (iii) both(i) and (ii).

As used herein, the term “exothermic data” refers to temperature dataobtained using a semi-adiabatic, or, more preferably, heat data obtainedusing an isothermal calorimeter (see e.g., commercially-available TAM®Air Calorimeters). Typically, the heat output is summed over a 24 or 48hour period, but may be measured for a longer period of time. A personskilled in the art of cement hydration will understand that accuratelymeasuring the total heat output is not a trivial exercise. The measuredheat output is quite variable depending on how fast the operatorperforming the test can properly mix the cement with water and place thesample in the calorimeter, as well as the difference in temperaturebetween the calorimeter and the materials. Total heat output can becalculated by summing the heat output starting from an initial period oftime (e.g. 1 hour, in which case the heat output from time=1 to 24 hoursis summed and considered the total heat), or alternatively, startingfrom a time corresponding to the minimum heat rate during the inductionperiod. The total heat generated is frequently correlated to a 1 daystrength for a given cement type (e.g. Blaine, chemistry, etc.).

The time values corresponding to specific events during the heatevolution can provide an indication of the sulfate-aluminate balance.Sulfate (frequently in the form of gypsum) is added to the crushedclinker so that when water is added, the sulfate reacts with thealuminate phases in the crushed clinker. This is the primary aluminatereaction and happens on the order of seconds after the water is combinedwith the cement. Based on the amount and solubility of the gypsum (i.e.plaster is more soluble than gypsum, and as temperature increases,gypsum becomes less soluble), this primary aluminate reaction can becontrolled, which allows a silicate reaction to proceed. This silicatereaction is the main contributor to the cement (and therefore concrete)strength gain. In most cases, a silicate peak is visible when looking atthe heat flow rate over time during a calorimetry test (see e.g., the“X’ in FIG. 5C). The time at which this occurs is T₁. If thesulfate-aluminate balance is sufficient, a renewed aluminate reactionwill occur.

FIGS. 5A-E help to illustrate various hydration scenarios that can ariseby application of calorimetry testing. The figures illustrateundersulfated to oversulfated states when the amount of sulfate mixed inwith a ground clinker is changed. Based on how close the renewedaluminate reaction is to the silicate reaction, the onset can be quitevisible, or, on the contrary, it can be difficult to discern. It can berevealed as a hump or shoulder (see hash mark appearing at 11.23 hoursin FIG. 5D), or a clear second peak (see hump appearing to right of hashmark appearing at 10.05 hours in FIG. 5C). Many methods exist fordetermining the onset of the renewed aluminate peak, (e.g., ASTMC563-17, ASTM C1679-17). Determination of onset is best when done on aconsistent basis (and, in this case, the T₂ is identified as the localmax of a 2^(nd) derivative of the heat flow curve).

In a first aspect of the thirteenth example embodiment above, the methodof the present invention may involve, in addition to use of the NIRsensor output, other information such as the gypsum amount/feed rate, orother predictions (such as the predicted gypsum amount, the predictedplaster amount, the predicted C₃A content, the predicted amount of theorthorhombic form of the C₃A mineral) that can also be combined with theNIR signal output value to provide a more accurate prediction of theDelta value. These other predictions can be provided based on the NIRsignal or other means (such as periodic XRD or XRF measurements).

The orthorhombic form of C₃A is interesting as it is remarkably morereactive in the presence of sulfate than is the alternate cubic crystalform. Its content is controlled by the complex balance of sulfate andalkali in the kiln, which can be affected not only by the raw materialcomposition, but also by changes in the fuel sulfur level as well as byreducing conditions in the kiln, which tend to deplete the sulfatecontent by promoting formation of sulfur dioxide gases which exit thekiln and are not incorporated into the clinker. Due to the complexity ofthese interactions, unexpected changes in the orthorhombic C₃A componentcan occur in relatively short time frames. The processor can beprogrammed to make a comparison between a combination of output signalsfrom the NIR sensor as well as, for example, C₃A orthorhombic content(supplied by XRD, for example), to data stored in processor-accessiblememory, the stored data previously obtained by irradiating finishedcements to sense an output NIR signal and accessing a C₃A orthorhombiccontent. Based on this comparison, a prediction of a physical orchemical property of the corresponding finished cement can be made, orthe current prediction can be refined and updated.

In a fourteenth example embodiment, which may be based upon any of thefirst through thirteenth example embodiments above, the inventionprovides a method wherein, in step (C), the stored reflected IR datacorresponds to exothermic data comprising calorimetric measurements ofhydrating ground finished cement; the method further comprising:

determining whether the difference between the time T₂ minus time T₁ isless than (−)1 hours or greater than (+)4 hours, where T₁ represents thetime at which maximum silicate reaction rate occurs after initiation ofcement hydration and T₂ represents the time after initiation of cementhydration at which either the renewed tricalcium aluminate reaction rateoccurs (if after T₁) or at which the aluminate reaction is completed (ifoccurring before T₁); and, if the difference of T₂ minus T₁ is less than(−)1 hours or greater than (+)4 hours, adjusting the (i) amount, form orboth amount and form of calcium sulfate introduced into the grindingmill; (ii) classifier settings, thereby to change relative amounts ofground particles being sent to the silo and being recirculated back intothe grinding mill; (iii) amount, type, or both amount and type of cementadditives introduced into the grinding mill; (iv) amount of water beingintroduced into the grinding mill; (v) amount of air provided byadjusting power or speed of a fan or blower connected to ventilate themill; (vi) amount or type of supplemental cementitious materialintroduced into the grinding mill; (vii) cement cooler setting, therebyto change the temperature of the finished cement or (viii) combinationof any of the foregoing.

In a first aspect of the fourteenth example embodiment, the methodinvolves determining whether the difference between time T₂ minus timeT₁ is less than 0 and greater than 3 hours; and, if the difference isless than 0 and greater than 3 hours, then any of the aforementionedadjustments or combination of adjustments can be made, based upon any ofthe aforementioned grinding mill conditions.

In a second aspect of the fourteenth example embodiment, the methodinvolves determining whether the difference between time T₂ minus timeT₁ is less than 0.5 and greater than 2.5 hours; and, if the differenceis less than 0.5 and greater than 2.5 hours, then any of theaforementioned adjustments (or combinations thereof) can be made, basedupon any of the aforementioned grinding mill conditions. A Delta between0.5 and 2.5 hours typically maximizes the 1 day strength of the clinker.This range shifts if other performance targets are desired, for example,if later age strength are to be maximized, the Delta should be increasedby 1-2 hours. Once the finished cement reaches the customer, addition offly ash or clays (e.g. calcined clays) to the concrete mix can addadditional aluminates to the cementitious system. In this case, thesulfate-aluminate balance will be shifted. A shift can also occur if thecement is cast at elevated temperatures. In this case, the increasedtemperature increases the reactivity of the aluminate, but decreases thesolubility of the sulfate. This leads to an under-sulfated situation. Inorder to prevent this situation from occurring, the Delta target in thecement plant may be shifted to the right (increased). Thus, inputs fromthe field can be used to adjust the target Delta.

In a fifteenth example embodiment, which may be based upon any of thefirst through fourteenth example embodiments above, the inventionprovides a method wherein, in step (C), the stored reflected IR datacorresponds to exothermic data comprising calorimetric measurements ofhydrating ground finished cement; the method further comprising:

determining whether the difference between the time T₂ minus time T₁ isless than the predefined target minus 1 hour or greater than thepredefined target plus 2 hour, where T₁ represents the time at whichmaximum silicate reaction rate occurs after initiation of cementhydration and T₂ represents the time after initiation of cementhydration at which either the renewed tricalcium aluminate reaction rateoccurs (if after T₁) or at which the aluminate reaction is completed (ifoccurring before T₁); and, if the difference is less than the predefinedtarget minus 1 hour or greater than the predefined target plus 2 hour,(i) amount, form or both amount and form of calcium sulfate introducedinto the grinding mill; (ii) classifier settings, thereby to changerelative amounts of ground particles being sent to the silo and beingrecirculated back into the grinding mill; (iii) amount, type, or bothamount and type of cement additives introduced into the grinding mill;(iv) amount of water being introduced into the grinding mill; (v) amountof air provided by adjusting power or speed of a fan or blower connectedto ventilate the mill; (vi) amount or type of supplemental cementitiousmaterial introduced into the grinding mill; (vii) cement cooler setting,thereby to change the temperature of the finished cement or (viii)combination of any of the foregoing.

In a first aspect of the fifteenth example embodiment, the methodinvolves determining whether the difference between time T₂ minus timeT₁ is less than the predefined target minus 0.5 hours or greater thanthe predefined target plus 1.5 hours; and, if the difference is lessthan the predefined target minus 0.5 hours or greater than thepredefined target plus 1.5 hours, then any of the aforementionedadjustments (or combinations thereof) can be made, based upon any of theaforementioned grinding mill conditions.

In a second aspect of the fifteenth example embodiment, the methodinvolves determining whether the difference between time T₂ minus timeT₁ is less than the predefined target minus 0.25 hours or greater thanthe predefined target plus 1 hour; and, if the difference is less thanthe predefined target minus 0.25 hours or greater than the predefinedtarget plus 1 hour, then any of the aforementioned adjustments (orcombinations thereof) can be made, based upon any of the aforementionedgrinding mill conditions.

The optimum Delta to maximize the strength is variable. While it isfrequently in the time ranges identified above, so they representappropriate starting point targets, various factors can alter it. Forinstance, if aluminate activity in the clinker or SCM increases, but thesulfate in the cement is in the form of more slowly soluble gypsum, agreater amount may be needed to increase the amount of sulfate that candissolve at early times, and thus control the very early aluminatereactions so the silicate hydration is not restricted. This greateramount of gypsum for optimum strength would lead to a greater Delta,even though the actual time this extra gypsum was needed was muchearlier. The ability of the present invention to detect such a change inclinker or SCM composition and adapt composition or mill controlsettings to accommodate the change is one of its key advantages.

In a sixteenth example embodiment, which is based upon any of the firstthrough fifteenth example embodiments, the method further comprisescomparing sensor data taken from step (B) to at least two differentstored processor-accessible data sets. For example, in step (C), thesensor output signals obtained in step (B) are compared to two differenttypes of stored data relating to different cement attributes orproperties; or, as another example, relating to two different timeperiods from which the data was collected. It is possible thatadjustments to processing conditions to change the strength will resultin changes to Delta and vice versa. For example, if the Blaine specificsurface area is increased to increase the strength, the ground clinkerwill become more reactive in terms of the aluminate phases, which willshift the Delta to lower time values. Thus, more sulfate may be added tocompensate. Preferably, comparisons and subsequent adjustments are madein an iterative fashion.

In a first aspect of the sixteenth example embodiment, the at least twoor more comparisons made in step (C) are further compared withrespective targets; and based on the deviations from the respectivetargets, a processor selects adjustments and the order of adjustments,wherein the adjustments comprise (i) the amount, form or both amount andform of calcium sulfate introduced into the grinding mill in step (A);(ii) the classifier setting, thereby to change relative amounts ofground particles being sent to the silo and being recirculated back intothe grinding mill; (iii) the amount or type of cement additivesintroduced into the grinding mill; (iv) the amount of water beingintroduced into the grinding mill; (v) the amount of air provided byadjusting the power or speed of a fan or blower connected to ventilatethe mill; (vi) the amount or type of supplemental cementitious materialintroduced into the grinding mill; (vii) the cement cooler setting,thereby to change the temperature of the finished cement, or (viii) acombination of any of the foregoing.

In a seventeenth example embodiment, which may be based upon any of thefirst through sixteenth example embodiments above, the inventionprovides a method further comprising measuring the particle size of theclinker and calcium sulfate being ground in the grinding mill; and, infurther response to the step (C) comparison between the obtainedreflected IR data and the stored reflected IR, adjusting a particle sizecharacteristic or property of the clinker and calcium sulfate beingground, or both.

In a first aspect of this seventeenth example embodiment, IR data, andmore specifically, NIR data is used to predict a particle sizecharacteristic of the ground cement, such as specific surface area(measured as, for example, Blaine), average particle size, D_(×10),D_(×50), D_(×90), D_([4,3]), D_([3,2)], span 90−10, −32 micron, −45micron, specific surface area, alpine (See e.g., M. C. Pasikatan et al.,J. Near Infrared Spectrosc. 9, 153-164 (2001)), and the method involvesmaking an adjustment to change particle size characteristic ordistribution. If detected IR values do not match stored valuescorresponding to a desired particle size, for example, an adjustment canbe done by altering classifier settings so as to obtain the desiredparticle size characteristic.

In a second aspect of this seventeenth example embodiment, data based onlaser diffraction measurements can be similarly used to predict particlesize characteristics of the ground cement, and similarly this can becompared to stored values, such that if measured laser diffractionvalues do not match stored laser diffraction values corresponding to adesired particle size characteristic, for example, an adjustment can bedone by altering classifier settings so as to obtain the desiredparticle size characteristic(s).

In a third aspect of this seventeenth example embodiment, periodic datacollected using the LD sensor system, which may be offline, can be usedto update or refine the NIR calibration for prediction of a particlesize characteristic of the ground cement.

In a fourth aspect of this seventeenth example embodiment, periodic datacollected using a temperature sensor, moisture sensor, XRD, XRF, PGNAAor a combination thereof, which may be offline, can be used to update orrefine the NIR calibration for prediction of a particle sizecharacteristic of the ground cement. For example, XRD, XRF, PGNAA maygive indications of iron which can help interpret the NIR signal.

In an eighteenth example embodiment, which may be based upon any of thefirst through seventeenth example embodiments above, the inventionprovides a method further comprising calculating a value correspondingto degree or level of prehydration of the cement, incorporating thevalue into processor-accessible memory, and initiating a decisionwhether to adjust the grinding mill or recirculation process conditions,and adjusting at least one of grinding mill or recirculation processconditions. For example, in step (B), the at least one energyradiation/sensor system is an infrared sensor system having an infraredemitter to irradiate the ground blend of particles or finished cementand an infrared sensor to detect infrared radiation reflected (IR) fromthe irradiated ground blend of particles or finished cement, theinfrared sensor system thereby obtaining reflected IR data; and, in step(C), the processor compares the reflected IR data with stored reflectedIR data corresponding to test result data indicating the degree or levelof prehydration the cement.

In a first aspect of this eighteenth example embodiment, the methodinvolves comparing output signal from IR sensor to stored data andcalculating the degree or level of cement prehydration, the stored databeing previously obtained by heating cement samples and measuring theweight loss within a defined temperature range. The prehydration levelis most accurately measured using a thermogravimetric analysis (TGA)instrument.

The quantitative measurement of “prehydration” level may be betterappreciated with reference to FIG. 7, which illustrates both the weightchange of cement as a function of temperature as well as the derivativeof the change in weight with respect to temperature as the cement isheated from room temperature to at least 450° C. The prehydration level,designated by the symbol Wk, defined as the percentage mass loss of acement sample as it is heated, starting just after the gypsum finishesdehydrating (about 125° C. in the example in FIG. 7) and finishing justbefore the portlandite (calcium hydroxide Ca(OH)₂) starts to decompose(about 350° C. in the example in FIG. 7).

In a second aspect of the eighteenth example embodiment, based on theprehydration level measurement (e.g., Wk), an adjustment is made to (i)the amount of water being introduced in the grinding mill in step (A),(ii) the amount of chemical additive introduced in the grinding mill instep (A), (iii) the amount of air provided (by adjusting the power orspeed of the fan connected to ventilate the mill); (iv) the amount ofcooling provided by the cement cooler; or (v) a combination thereof.

In a third aspect of the eighteenth example embodiment, a furthercomparison is made, which is based on a predefined relationship betweenthe prehydration level and the Delta of the cement, the amount and/ortype of sulfate (which is determined based on the comparison made instep (C)) is adjusted in response to a change in the measuredprehydration level of the cement (which is based on a separatecomparison made in step (C)), to correct Delta value so that it moreaccurately corresponds to or matches a predetermined target value. Thiscan be performed as an iterative process.

In a fourth aspect of the eighteenth example embodiment, based on apredefined relationship between prehydration level and the strength ofthe cement (e.g. at the age of 1 day), the fineness or other parameters(as previously discussed) affecting strength is adjusted in response toa change in the measured prehydration level of the cement, to controlthe strength up or down to match a predetermined target value. This canbe performed as an iterative process.

In a nineteenth example embodiment, which may be based upon any of thefirst through eighteenth example embodiments above, the inventionprovides a method wherein, in step (B), the at least one energyradiation/sensor system is an infrared sensor system having an infraredemitter to irradiate the ground blend of particles or finished cementand an infrared sensor to detect infrared radiation reflected (IR) fromthe irradiated ground blend of particles or finished cement, theinfrared sensor system thereby obtaining reflected IR data; and, in step(C), the processor compares the reflected IR data with stored reflectedIR data corresponding to test result data, and indicates on a monitordisplay, print out, or by visual or audible alarm when the degree ofreduction in the clinker meets or exceeds a pre-established thresholdvalue.

A reducing kiln environment (oxygen deficient) beyond a threshold canhave a significant detrimental effect on the performance (strength) ofthe clinker produced and the resulting cement. A number of factors caninfluence the development of reducing conditions. Changes in raw mealcomposition, grind (size) and feed rate (and flow) can affect the oxygenconsumption rate and thus the conversion of the kiln conditions fromoxygen-rich to oxygen-deficient environment, without any changes to theother variables in the system. The other variables which can alsoinfluence the kiln conditions include changes in fuel type (calorificvalues, coal to petcoke, use of alternative fuels) and changes to kilnprocess (flame position and shape, air flow rates and sources,temperature etc.). To train the NIR to predict reduction, the testresult can be obtained from a chemical reduction test such as theMagotteaux test (see e.g., Hardtl, R., “Magotteaux test for cementanalysis, in Betonwerk+Fertigteil-Technik, Vol. 69 (2003), or Manns, W.,“Zur Braunverfärbung von Betonwaren—Möglichkeit der frühzeitigenErkennung,” Betonwerk+_Fertigteil-Technik, Vol. 68 (2002)), or onresults from chemical analysis such as XRD, XRF, and even furthermorefrom microscopy analysis.

In a twentieth example embodiment, the present invention provides asystem for manufacturing cement, comprising:

a grinding mill for grinding raw materials including clinker, a sourceof sulfate chosen from gypsum, plaster, calcium anhydrite, or a mixturethereof, and optionally cement additives, to produce a ground blend ofparticles comprising ground clinker and calcium sulfate;

a classifier for separating the ground blend of particles whereby afirst portion of the particles or the finished cement are sent to a siloor other receptacle for containing the finished cement and whereby asecond portion of the particles is recirculated into the grinding millfor further grinding;

at least one sensor system chosen from infrared sensor system, laserdiffraction sensor system, or both, for detecting emanation,reflectance, transmittance, or absorption of energy by or through theground blend of particles or finished cement, and generating outputsignals corresponding to the detected energy; and

a processor configured or programed to compare output signals generatedby the at least one sensor system with data stored inprocessor-accessible memory, the stored data comprising output signalvalues previously obtained from sensors measuring the emanation,reflectance, transmittance, or absorption of energy in the infraredspectrum, laser diffraction spectrum, or in both the infrared and laserdiffraction spectrums (the stored data being correlated with a physicalor chemical property of the corresponding finished cement, hydratedcement or cementitious product made with the cement, e.g., (i) strengthtest data, (ii) exothermic data; (iii) set initiation data; (iv) slumpdata; (v) dimensional stability data; (vi) air content data; (vii)prehydration level data; (viii) reduction or burn conditions data; (ix)cement particle size distribution data; or (x) a combination thereof);and

the processor further configured or programed to adjust (i) amount,form, or both amount and form of calcium sulfate introduced into thegrinding mill; (ii) classifier setting, thereby to change relativeamounts of ground particles being sent to the silo and beingrecirculated back into the grinding mill; (iii) amount, type, or bothamount and type of cement additives introduced into the grinding mill;(iv) amount of water being introduced into the grinding mill; (v) theamount of air provided by adjusting power or speed of a fan or blowerconnected to ventilate the mill; (vi) amount or type of supplementalcementitious material introduced into the grinding mill; (vii) thecement cooler setting, thereby to change the temperature of the finishedcement, or (viii) combination of any of the foregoing (e.g., in order tomodify a physical or chemical property of the finished cement).

In various exemplary aspects of the above-described nineteenth exampleembodiment, the system of the invention may incorporate variousexemplary features and aspects as previously described for the secondthrough eighteenth example embodiments as described above.

In a twenty-first example embodiment, which may be based on any of theforegoing first through twentieth example embodiments, the inventionprovides a method or system which comprises, steps and/or componentsfor:

(A) providing an indication (e.g., audible or visual alarm orindication, monitor or hand-held display, text message, email, etc.)that a physical or chemical property or amount of the raw materials, rawmeal, clinker, the source of calcium sulfate, the chemical additive, theSCM, or the finished cement has changed;

(B) performing at least one test to determine a physical or chemicalproperty on the finished cement chosen from (i) strength test data, (ii)exothermic data; (iii) set initiation data; (iv) slump data; (v)dimensional stability data; (vi) air content data; (vii) prehydrationlevel data (i.e., measurement of amount or degree of chemical changeand/or reaction product formed on cement particle surface due toreaction between absorbed moisture and certain phases of the cement);(viii) reduction or burn conditions data; (ix) cement particle sizedistribution data; and (x) a combination thereof;

(C) detecting from the finished cement tested in step (B) using at leastone sensor system chosen from infrared sensor system, laser diffractionsensor system, or both; the at least one sensor system providing outputsignals corresponding to the reflectance, transmittance, or absorptionof energy by or through the ground blend of particles or finishedcement; (D) storing both the test results of (B) and (C) into a databaseaccessible by a processor; and (E) making an adjustment to a modelpredicting at least one of physical or chemical properties listed abovein subparts (B(i)) through (B(ix)), making an adjustment to a targetvalue for at least one of (i) through (ix) or both.

In a first aspect of this twenty-first example embodiment, theindication is (i) a change in the fuel source; (ii) a predefineddeviation from a chemical property as measured by IR, LD, QXRD, XRF,PGNAA or a combination thereof; (iii) a predefined deviation in the milltemperature or humidity; (iv) a predefined deviation in the relative rawmaterials entering the kiln; (v) a change in a kiln processingcondition; (vi) a change in a mill processing condition; or (vii) anotification that a manual or automated cement sample was taken.

In a second aspect of this twenty-first example embodiment, the sampleis obtained via an autosampler and more preferably, a sample obtainedvia an autosampler that is not composited over time.

In a third aspect of this twenty-first example embodiment, theindication is a change in any predicted value derived from a comparisonbetween an IR signal and (i) strength test data, (ii) exothermic data;(iii) set initiation data; (iv) slump data; (v) dimensional stabilitydata; (vi) air content data; (vii) prehydration level data; (viii)reduction or burn conditions data or; (ix) cement particle sizedistribution data; or (x) a combination thereof.

In a fourth aspect of the twenty-first example embodiment, the model isadjusted by recalibrating the model with the new data. The comparisondescribed for step (C) of the first example embodiment can be performedthrough use of look up tables or by using algorithms configured togenerate predicted test results. For example, this can be done by usingthe NIR signal output value to identify a similar signal stored in thememory and retrieve the associated test result data. This can also bedone by using a mathematical function, based on the NIR, LD, T, M/RHsensor values, to generate a predicted test result value (e.g., astrength value). The algorithm or mathematical function can be derivedbased on standard regression techniques such as linear regression,partial least squares regression, regression techniques combined withprincipal component analysis or factor analysis approaches, or evenmachine learning, which includes both supervised (e.g. support vectormachines, Bayesian methods, random forest methods, etc.) andunsupervised machine learning methods (k-means clustering, neuralnetworks, etc.).

In a twenty-second example embodiment, which may be based on any of theforegoing first through twenty-first example embodiments, the inventionprovides a system and method of analyzing the performance of a cement,comprising: steps and/or system for (A) detecting from a ground blend ofparticles or finished cement obtained from step (A) using infraredsensor system output signals corresponding to the emanation,reflectance, transmittance, or absorption of energy by or through theground blend of particles or finished cement; (B) comparing, using aprocessor, output signals provided by the infrared sensor system to datastored in processor-accessible memory, the stored data previouslyobtained by detecting from the finished cements by at least one sensorsystem (the stored data being correlated with a physical or chemicalproperty of the corresponding finished cement, hydrated cement orcementitious product made with the cement, e.g., (i) strength test data,(ii) exothermic data; (iii) set initiation data; (iv) slump data; (v)dimensional stability data; (vi) air content data; (vii) pre-hydrationlevel data, or; (viii) reduction or burn conditions data; (ix) cementparticle size distribution data; and (C) returning a predicted physicalor chemical property of the corresponding finished cement.

In a first aspect of the twenty-second example embodiment, at least twophysical or chemical properties of the cement are predicted from theinfrared sensor system output signal.

The invention can be embodied in many different modes and should not beconstrued (nor should expressions regarding what the “invention is orprovides” be construed) as a limitation to the exemplary embodiments setforth herein; rather, these embodiments are provided so that thisdisclosure will be thorough and complete and fully convey the scope ofthe invention to those of ordinary skill in the art.

EXEMPLIFICATIONS

In a first example, an illustrative method and system of the inventionfor adjusting sulfate levels in grinding manufacture of cement isoutlined in the flow chart of FIG. 8 and illustrated in FIG. 9.

In block 102 of FIG. 8, a cement grinding mill (212 of FIG. 9) (e.g. aball mill, vertical roller mill, etc.) is fed a combination of clinker(214), a source of sulfate (e.g. gypsum) (216), and optionally one ormore SCMs (e.g. fly ash, slag) (218) and/or cement additives (e.g.strength enhancers, grinding aids, set modifiers, workability modifiers,sodium sulfate, chromium reducers) (220) at known rates, and exposedduring the milling process to a water spray (222) at a known rate and aventilation fan (224) set at a known speed. A computer processor (226)receives information about the feed rates and characteristics of eachcomponent (e.g. an identifying name). Furthermore, a near infrared (NIR)sensor (228) can obtain a reflection signal from the clinker, SCM andsulfate sources independently or as a group. These signals can be sentto and then analyzed by the processor via predetermined lookup tables orcorrelation functions to determine features such as alkali sulfates fromthe clinker; aluminate content from the SCM; gypsum/anhydrite ratiosfrom the sulfate source (plaster is formed by dehydration of the gypsumduring the milling process). For the chemical additive, identifiers(e.g. product name) or detailed information about the formulations (e.g.TEA content) can also be sent to the processor.

In block 104 of FIG. 8, the processor also receives information aboutthe mill output volume as well as NIR spectra (230 of FIG. 9), a laserdiffraction (LD) signal (232) and optionally a temperature, moisture orhumidity (234) from the finished cement exiting the mill or optionally atemperature, moisture or humidity (254) from the chimney (260). Thesesignals (including those on the SCM/sulfate/etc.) can be collected, forexample, every minute. The multiple real-time NIR spectra can becollected using the same NIR spectrometer via input from differentsensor channels. For example, the Bruker MATRIX-F FT-NIR spectrometerallows collection of signals from six different sensors. The signals arecollected using sensor heads that transfer the signal to thespectrometer using fiber optic cables which preserves signal quality.This allows not only multiple sampling points, but also, allows thespectrometer itself to be placed in a protected area free from cementdust and other harmful elements (e.g. humidity and heat).

For NIR sensors situated to monitor material being carried on belts, thedistance between the surface of the material (e.g., cement or SCM) bedand the sensor can vary with time as the material bed passes below thesensor. This can affect the measured NIR signal. A protective casingmade of an optically clear (e.g. low light absorbance) material such asquartz, sapphire, or glass can be used to submerge the sensor within thematerial particles. This can allow the distance between the sensor andthe material particles to remain constant.

Alternatively, a distance sensor, such as an ultrasonic range finder,can be installed next to the NIR detector so that a distance measurementcan be made and used to adjust the NIR measurement or prediction in realtime. Such a range finder is commercially available under theULTRASONIC® brand (See e.g.,https://www.maxbotix.com/Ultrasonic_Sensors.htm). Aside from thedistance of the material to the detector, the material bed depth shouldbe sufficient depending on the internal setup of the NIR instrument. Inmost cases, a bed height of more than 1 cm is sufficient.

Cement and other fine particulates can also be transported via apneumatic tube or with air slides or other air-flow channels instead ofa moving belt. In this case, an optically clear window can be installedin-line with the tube (or on a bypass tube connected to the tube). NIRsignals can then be collected. For NIR instruments situated to monitorair slides, the concentration of the fluidized bed particles may affectthe NIR signal. In this case, the NIR signal may be adjusted based onchanges in parameters such as the air slide flow rate.

Preferred for use in the present invention are IR detectors suited tomeasure diffuse light (e.g., light that is scattered by a particle bed).

The system may also include more than one NIR sensor. In one example,different NIR sensors may be programmed to only scan a narrow window ofwavelengths to improve the speed and/or accuracy at which the spectra iscollected. For example, one NIR may be dedicated to determine a gypsumamount while another may be dedicated to a Delta measurement. It may bethat different predictions of parameters (e.g. Delta or strength)require different spectral ranges or values. It is also possible toprogram a wavelength hopping scheme, where discrete regions of thewavelength spectra is collected instead of the entire spectra.

An example of an NIR signal is shown in FIGS. 10A-D. A raw signal isgiven in FIG. 10A over a wavenumber range between 4000 and 12000 cm⁻¹.The raw intensity is reported. In FIG. 10B, a standard, normal variatetransformation is applied to normalize the baseline. In FIGS. 10C and10D, the first and second derivatives are given respectively. In thegeneration of predictive models, one or more of these signals can beused as inputs for the model.

Based on the NIR signal, properties of the finished cement can bedetermined using lookup tables or correlation functions. Thesecorrelation functions or models can be generated using several standardtechniques including multiple linear regression, multivariateregression, principal component regression, partial least squaresregression, machine learning or other methods. For example, a well-knowntechnique used to develop NIR correlations (to species concentrations),is partial least squares regression (PLS). See e.g., Wold, S.; Sjöström,M.; Eriksson, L. (2001). “PLS-regression: a basic tool of chemometrics”.Chemometrics and Intelligent Laboratory Systems. 58 (2): 109-130, andU.S. Pat. No. 5,475,220, which is specific to cement phase analysis.Other approaches may involve for example, Fourier transforms (see, e.g.McClure, W. F.; Hamid, A.; Giesbrecht, F. G.; Weeks, W. W.; (1984).“Fourier analysis enhances NIR diffuse reflectance spectroscopy.”Applied Spectroscopy. 38 (3): 322-328), and machine learning methods(See e.g., Borin A.; Ferrão M. F.; Mello C.; Maretto D. A.; Poppi R. J.;(2006). “Least-squares support vector machines and near infraredspectroscopy for quantification of common adulterants in powdered milk.”Analytica Chimica Acta. 579 (1): 25-32).

These models or lookup tables are constructed by obtaining NIR signalsfrom multiple cement samples and measuring the desired property ofinterest (e.g. strength or setting time) for the corresponding hydratedcement samples (in the case of strength, for example) or unhydratedsamples (in the case of a fineness parameter or pre-hydration, forexample). As association is then made between the NIR signal and theproperty of interest, allowing the property to be predicted just fromthe NIR signal.

In addition to the predicted properties from the received NIR signals onthe finished cement, the LD signal is used to determine a finenesscharacteristic of the cement (e.g. specific surface area, mean particlesize, fraction below a certain sieve size, etc.). See e.g. the Insitecparticle size analyzers commercially available from Malvern. Thisfineness characteristic is more preferably obtained from the NIR signal.

Based on the predictions from the NIR and LD signals, the finishedcement produced can be adjusted towards one or more desired targets. Forexample, a finished cement may require to meet both a Delta target andstrength target. Although maximum targets can be specified, in general,a balance of multiple properties is desired, which may not be theoptimum for any one property. More desirable may be a consistent cementproduct. Thus, for example, a Delta of 2 hours with a strength of 42.5MPa may be a target for a given finished cement.

The target can be assigned in multiple ways depending on the cementproducer's preferences or needs. For example, a cement producer may beproducing a cement with a certain class of strength (e.g. class 42.5(minimum strength of 42.5 MPa, maximum strength of 62.5 MPa at the ageof 28 days). Targets for Delta can also be determined using standardssuch as ASTM C563-17 tests or equivalent. In these cases, it is possibleto use sulfate contents corresponding to the strength or calorimetryresults and combine these data with NIR signals of the correspondingcement (the NIR signals obtained before hydrating the cement). Theinventors have found that the optimum Delta, (i.e. the Deltacorresponding to the highest strength) can be predicted based on the NIRsignals. This provides an enormous advantage as both the target Deltaand the current Delta (with a given amount of sulfate added) can bepredicted in real-time. Currently, there is no method to provide areal-time optimum Delta. Still, the cement producer may also tailor, forexample, their Delta to the region or market that they are selling to.In warmer climates a higher temperature may lower the solubility ofplaster. If plaster has been used to control rapid aluminate reaction asulfate deficiency may result. Furthermore, the reactivity of thealuminates increase, which can greatly increase the susceptibility toflash setting or extended set. Therefore, the cement producer may wantan increased Delta. As another example, if the cement producer's markettypically produce cement which is later combined with high volumes ofclass C fly ash, an increased Delta may also be desired to avoid commonflash setting or extended set with class C fly ash (as the fly ashcontributes more aluminate to the overall cementitious system withoutenough sulfate to balance). Or, the cement producer may decide to makean adjustment to the NIR-predicted optimum Delta. In other words, as theNIR-predicted optimum may indicate the Delta required to optimizestrength, the producer may want to increase the Delta by, for example, 1hour from this optimum Delta in order to account for the region (e.g. awarmer climate where the Delta will be reduced) or market (e.g. wherefly ash is frequently added to the concrete and will supply extraaluminate that will lead to a reduced Delta). Targets may also beassigned to meet other related constraints, such as cost, carbon dioxideemissions, workability retention, admixture response, achievement ofrequired early strength without exceeding statutory maximum strength,etc.

In FIG. 11, optimum Delta values predicted based on NIR signals arecompared to actual measured optimum Delta values on the correspondingcements. Ten individual clinkers were crushed in a laboratory ball mill.Each crushed clinker was then blended with various levels of gypsum andplaster. For each blend, an NIR signal was obtained using a BrukerMATRIX-F FT-NIR spectrometer. Output signals similar to those in FIGS.10A through 10D were obtained. In addition, for each blend, a mortarspecimen was created according to EN-196-1:2016, which includes mixingwith a standard sand sample and water to cement ratio. Variousproperties such as workability, air, strength, and Delta were obtained.Delta values were obtained through analysis of heat flow curvesgenerated by a TAM® Air Calorimeter, generating output signals similarto those of FIGS. 5A through 5E. In order to develop the NIR outputsignal—optimum Delta relationship shown in FIG. 11, the maximum strength(in this example, the compressive strength after 1 day) was determinedfor a set of crushed clinker with different sulfate levels, each with adifferent, measured Delta. The optimum Delta, therefore, corresponds tothe maximum strength attained. This optimum Delta is valid for a givenclinker (which was produced at a given instance in time). Data setsincluding the NIR output signals and the measured optimum Delta valueswere partitioned into cross-validation sets, using a repeated-stratifiedK-fold method. For each set, a partial least-squares (PLS) model was fitto a training partition, and validated on the remaining data (thetesting partition). In implementing the PLS model, the number ofcomponents yielding the best fit according to the average accuracy overall the cross-validation sets was chosen. This PLS was then applied toall of the data and the fit is shown in FIG. 11. In FIG. 11, thepredicted optimum Delta is plotted against the actual measured optimumDelta, with the solid line representing a one-to-one relationship. Forthis particular model, applied over 432 points, over 91% of thepredicted values were within 0.5 hours of the actual measured values.Note that this prediction is valid over a large range of clinkerchemistries and physical properties (e.g. Blaine specific surface area).

In addition to targets given for Delta and strength based on, forexample, NIR predictions or fineness characteristics, ancillary limitscan be provided to prevent certain processes from leading to suboptimalcement properties or mill conditions. For example, a maximum and minimumgypsum feeder rate, or rates of change of such feeder rate can beestablished. Likewise, limits on the water spray and ventilation fanspeed can be enforced. Because the relationships between for example thewater spray, pre-hydration level, and gypsum dehydration can be complex,limiting the process can limit unexpected interaction issues (e.g. thewater spray rate or the cement cooler may affect both temperature andmoisture in the mill). These limits can help to prevent runawayconditions where catastrophic results may occur.

In order to achieve the targets, predictions of both Delta and strengthmay be determined. In FIG. 12, Delta values predicted based on NIRsignals are compared to actual measured Delta values on thecorresponding cements. The model was generated using the same cementsets that the optimum Delta was calculated from, which again includesten individual clinkers. For this particular model, applied over 365points representing ten individual clinkers, 98% of the data waspredicted within 0.5 hour of the actual measured value. Note that thishas been performed over a very wide range of clinker chemistries(represented by the shape of the data point), sulfate levels and Blainespecific surface areas (represented by the shade of the data point) andsurprisingly has shown a very high accuracy. It is expected that withina given plant, the range of both clinker chemistries and specificsurface areas will be narrower than the data used to generate FIG. 12,which may lead to improvements in accuracy. Thus, based on theprediction, a current Delta value of the finished cement can bedetermined. Based on a deviation from the target, several differentoptions can be taken. For example, in the event that the Delta isgreater than the target, the sulfate content can be reduced. The amountof reduction can be determined based on a predetermined relationshipbetween a sulfate dose and Delta. However a more preferred method is tomake a small adjustment in the sulfate content (in this case areduction) that is large enough to be detected by the NIR signal, butsmall enough not to cause a catastrophic change in the cement properties(i.e. to avoid under- or over-dosing). After the change has been made,another NIR signal and prediction can be executed to measure thedeviation of the Delta with respect to the target. This process can berepeated until the Delta is within a predefined distance from thetarget. A similar process can be performed if the Delta is less than thetarget (e.g. the sulfate source can be incrementally increased).Moreover, the invention allows not only the total sulfate, but theamounts of gypsum and plaster to be adjusted. The plaster content is notas straightforward as adding or subtracting the sulfate source becausethere are cases where a given total sulfate content is required alongwith a specific gypsum to plaster ratio. In these cases, changes to themill processing parameters can be performed, thus affecting the amountof gypsum dehydration to plaster. For example, if the gypsum/plasterratio is to be decreased, the temperature in the mill can be increasedand/or the water spray rate can be decreased. However, the mill systemis complex and this action may affect pre-hydration or other factorsaffecting strength. It is with such system complexities that a real-timemeasurement of both Delta and strength enables true control.

As another method of control, an evolutionary optimization scheme can beimplemented. Evolutionary optimization is an artificial intelligencealgorithm inspired by biological evolution. Related to the presentinvention, small actions, which may be random, are taken to introduce achange to the cement production process. Measurements are made (throughthe use of NIR, for example) to determine the effects of the smallactions. Because measurements can be made in real-time, many smallactions can be taken. Each action and measurement is recorded and thealgorithm begins to learn the best way to optimize toward a pre-definedgoal, for example to achieve a strength target of 42.5 MPa and a Deltaof 2 hours. This method provides an advantage over a traditionaloptimization method, since traditional methods rely on understandingaccurate relationship between actions and the changes measured (e.g.increasing Blaine and achieving a certain change in strength as measuredby NIR). Because of the complexity of the system, understanding both therelationships and the interaction effects (e.g. changes in Delta as theyaffect changes in strength and vice versa) is very difficult.

As a second example, in FIG. 13, strength values predicted based on NIRsignals are compared to actual measured strength values on correspondingcements. The model was generated using the same cement sets that theDelta was calculated from, which again includes ten individual clinkers.In this case, after the PLS model was generated (in the same fashion asdescribed above), 77% of the predicted values fall within 5% of theactual measured strength. This is a surprisingly high degree of accuracyconsidering that the correlation function used to make the predictionwas developed using a wide range of clinker chemistries (represented asthe shape of the data point) and Blaine specific surface areas(represented as the shade of the data point). It is expected that whenthe range of variation in the clinker and cement properties is lower, aswould be expected when only measuring the cement made from a singleplant using clinker from the same kiln, the accuracy should improve.This is supported by FIG. 14, which shows that the accuracy is higherwhen only one clinker source is considered, at similar Blaine specificareas (98% of the predicted values fall within 5% of the actual measuredstrength). To the inventors' knowledge, a direct relationship betweenstrength and NIR signals has not previously been demonstrated.

With a strength prediction as shown in FIG. 13 or 14, not only can thedeviation from a target strength be determined, but the change instrength relative to the Delta can also be determined. Thus, aniterative approach is possible where both the predicted Delta andstrength are constantly monitored in conjunction with other possiblemeasured parameters, and adjusted, leading to an understanding of howoptimum Delta varies with other factors. The present invention enablesthis on a frequency basis on the order of minutes, which is of the sameorder of magnitude as the cement residence time in the mill.Furthermore, this also enables each adjustment (to one or moreparameters/processes) to be of small increment, because the applicationof online sensors allow prediction of both Delta and strength amultiplicity of times over a short period of minutes, strengthening thestatistical confidence in the direction of performance change broughtabout by said small adjustment. Having confidence in the result of theadjustment, further adjustments can be made. Such a method allows arapid iterative process to accommodate changes in the clinker, sulfatesource, SCM, additive performance, etc. This is a distinct improvementover what is available to cement producers today. For example, if acement producer were today using calorimetry to control to apre-determined optimum Delta, the Delta could be known at best every8-16 hours (depending on when the Delta actually occurs in the cement).This has two distinct disadvantages. Firstly, the clinker, sulfatesource or SCM composition, cement fineness, and other properties maywell have changed in the 12 hours since the cement sample was collected,so the adjustment indicated by the calorimetry test may no longer be thecorrect or optimal one. In other words, the target or optimal Delta isassumed to be constant for a clinker even through the chemistry of theclinker, sulfate source, or SCM, or fineness of the cement has changed,thus possibly resulting in a change of the optimal Delta. Secondly, ifthe calorimetry indicates that Delta is far off from the optimal value,then this means that sub-optimal cement has been produced for the past12 hours. In the case of optimizing and adjusting based on strengthmeasurements, this problem is even worse, since, by definition, itrequires at least 24 hours to obtain a 1-day strength measurement.

Moreover, when considering management of more than one parameter (inthis case, strength and Delta), the inability for real-time monitoringin current practice makes the control even more difficult. For example,in order to adjust Delta, a calorimetry test must be performed, whichtakes 8 hours at minimum. After the result is received, an adjustment ismade for example, to the sulfate feed rate. After this has occurred,another sample must be taken to determine the effect on strength. Thistest takes 24 hours. If an adjustment is made to strength, then theDelta must be rechecked, which takes another 8 hours. Thus, a complete“cycle” of adjustments takes 40 hours with the current technology. In 40hours, for instance, 4000 MT of cement can be made, and as was stated inthe previous paragraph, it is possible that the composition of theclinker, sulfate source or SCM has already changed. Further, due to thelong lead-time, larger changes must be made, with increased risk that itis not in the right direction. A real-time measure and manage systemapplied iteratively circumvents these issues and allows the cementproducer to produce a consistent product.

A real-time solution is especially necessary if changes are made outsideof the mill, i.e. in the kiln. Based on the chemistry of the clinker asdetermined by an NIR sensor on the cement produced, or on the stream ofclinker entering the mill, it may be desirable to make changes to theraw material ratios into the kiln. This would be much less advantageousif accomplished at intervals of 8 or more hours (i.e. as is possiblewith calorimetry today). Aside from changes in the kiln raw meal,changes in the processing can also be done based on clinker and finishedsample monitoring.

During the classification of the cement within the classifier (236 ofFIG. 9), coarse particles are recirculated back to the mill (238) whilefiner particles are transferred to the cement silo (250) as finishedcement (244). In block 106 of FIG. 8, a LD signal from the recirculatedportion can be obtained. Based on this signal, a fineness characteristiccan be calculated, which, when combined with a fineness characteristicof the finished cement, can be used to determine how to change theparticle size distribution of the finished cement. Control of theclassifier includes several methods: air speed, volume loading, etc.Based on the combined LD signals, one method may be more preferentialthan another. Alternatively, an NIR sensor can replace or augment the LDsensor to also provide a fineness characteristic.

It is also envisioned that an acoustic sensor that monitors the grindingmill can provide information to the filling (of steel balls) of themill. This information may be useful for particle size adjustments.

Another beneficial feature enabled by real-time monitoring andmanagement of the cement process is the ability to selectively makecertain properties constant. This is an advantage from a modeling pointof view, as predictions can become more accurate. For example, in FIG.15, Delta was held constant while strength was predicted. Compared toFIG. 13, the cross-validation accuracy improved 6 percentage points.Thus, it may be advantageous to first adjust the Delta to the desiredtarget and then adjust strength (iteratively). Alternatively, the Blainespecific surface area can be held constant (or at least the variationcan be minimized through a closed-loop control system, for example). Inthis case, again, the improvement in the strength prediction can bedemonstrated.

In block 108 of FIG. 8, a temperature (T), moisture (M) or relativehumidity (RH) sensor (or a combination thereof) (234 or 254 of FIG. 9)can be used to give an indication of the gypsum dehydration. Thisinformation can be used to correct for the dehydration by adjusting thesulfate feed rate or other mill processes (e.g. water spray) to adjustthe ratio between gypsum and plaster. An NIR sensor can also be used todetermine the temperature, moisture or relative humidity or even thedehydration rate directly. Similarly, in block 108 of FIG. 8, theconduit (e.g. belt or air slide) between the mill and the silo can beinstrumented with T, M, RH sensors or a combination thereof (234 of FIG.9), or the cement cooler between the mill and the silo to monitor thedehydration during the transportation to the mill. And finally, the sametype of sensors can be instrumented in the silo itself (250) to providecorrection factors due to the dehydration, as shown in block 108 of FIG.8. Again, an NIR sensor can be used to collect similar information.

Aside from gypsum dehydration, it is envisioned by the inventors thatpre-hydration can also be predicted from T, M, RH or NIR sensorsreadings in these same locations.

The performance of cement additives depends on sulfate type and content(gypsum, hemihydrate, anhydrite), on cement fineness and on the degreeof cement pre-hydration. Therefore, modifications on the type and dosageof the cement additive need to consider the advantages and disadvantagesof changing other factors. The next four examples illustrate some ofthese relationships.

Cement additives can affect Delta. A reduction in Delta may happen wheningredients that chelate aluminum (such as alkanolamines or sugars) arepresent in the cement additive. A higher sulfate content can ensureDelta is within the preferred range for maximum strength. Adapting to aDelta or to a compressive strength target may therefore involve changingthe composition of the cement additive and/or adjusting the content ofsulfate.

FIG. 16 shows the compressive strength at 1 day of EN-196-1:2016 mortarsprepared with a cement ground in the laboratory using an industrial ASTMC 150 type II/V clinker as a function of the active dose of disodiumethanol diglycinate (Na₂-EDG; dose in ppm of cement) and the addedcontent of SO₃ (as gypsum and plaster). 3325 grams of crushed clinkerwere ground with 63.5 grams of gypsum and 39.4 grams of plaster in alaboratory ball mill to a Blaine specific surface area of 3,400 cm2/g toproduce an initial cement with 1.50% SO₃. The SO₃ weight ratio of thisgrind is 1:0.74 gypsum:plaster). The two other levels of SO₃ (2.02% and3.08%) were obtained by dry blending gypsum and plaster in the same SO₃weight ratio as the initial cement prior to the mortar mixing. The graphshows that there is 1.5-2.0 MPa strength decrease of for every level ofSO₃ added and the performance trend of Na₂-EDG is independent of thechanges in SO₃ content in the range tested.

In the next example, FIGS. 17, 18 and 19 show the compressive strengthat 1 day of EN-196 mortars prepared with cements ground in thelaboratory using industrial ASTM C 150 type I or I/II clinkers as afunction of both the active dose of different additives in ppm of cementand SO₃. The strength response is represented as a contour plot. Toproduce these samples, 3325 grams of crushed clinker were ground in alaboratory ball mill to a Blaine specific surface area of either 3,300or 4,300 cm2/g without any source of calcium sulfate. The levels of SO₃tested for each clinker were obtained by dry blending gypsum and plasterto the ground cement prior to the mortar mixing.

FIGS. 17 and 18 compare two different additives (diethanolisopropanolamine (DEIPA), and triethanol amine (TEA)) within the samecement. The contour plots in FIGS. 17 and 18 demonstrate the complexityof the additive efficiency, as it depends on both the additive dosageand the sulfate content for Cement 2. The present invention can ensurethat the proper ranges of both are satisfied to maximize efficiency ofthe additive. In FIG. 19, DEIPA is added to a different cement (Cement3). In comparing FIGS. 17 and 19, it is demonstrated that the responseis different depending on the cement. Thus, in order to optimizeadditives for properties such as strength, real-time sulfate andstrength predictions based on for example, and NIR signal, can help todetermine optimal additive dosages. For example, adjustments can be madeto move the system in a certain sulfate range.

The principles, preferred embodiments, and modes of operation of thepresent invention have been described in the foregoing specification.The invention which is intended to be protected herein, however, is notto be construed as limited to the particular forms disclosed, sincethese are to be regarded as illustrative rather than restrictive.Skilled artisans can make variations and changes without departing fromthe spirit of the invention.

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 21. A method for manufacturing cement, comprising: (A) introducing, into a grinding mill, raw materials comprising clinker, a source of sulfate chosen from gypsum, plaster, calcium anhydrite, or a mixture thereof; grinding the raw materials, to produce a ground blend of particles comprising ground clinker and calcium sulfate; and separating the ground blend of particles within a classifier whereby a first portion of the particles or the finished cement is removed from the grinding mill and whereby a second portion of the particles is recirculated for further grinding in the grinding mill; (B) providing at least one infrared sensor system comprising a processor that is communicative with processor-accessible memory, the at least one infrared sensor system effective to detect at least one of emanation, reflectance, transmittance, or absorption of infrared energy in the range of 700-1400 nm by or through the ground blend of particles or finished cement provided in step (A), and generating output signals corresponding to the detected energy; (C) comparing, using the processor, the output signals generated in step (B) to data stored in the processor-accessible memory, the stored data comprising output signal values previously obtained from the at least one infrared sensor system measuring the emanation, reflectance, transmittance, or absorption of energy in the infrared spectrum of 700-1400 nm, the stored data being correlated with a physical or chemical property of the corresponding finished cement, hydrated cement, or cementitious product made with the cement; and (D) in response to the comparison in step (C), adjusting a grinding mill condition chosen from (i) adjusting amount and form of calcium sulfate introduced into the grinding mill in step (A); (ii) adjusting classifier settings thereby to change relative amounts of ground particles being removed from the grinding mill and being recirculated back into the grinding mill; (iii) adjusting amount, type, or both amount and type of cement additives introduced into the grinding mill; (iv) adjusting amount of water being introduced into the grinding mill; (v) adjusting amount of air provided by adjusting power or speed of a fan or blower connected to ventilate the mill; (vi) adjusting amount or type of supplemental cementitious material introduced into the grinding mill; or (vii) performing a combination of the foregoing adjustments of grinding mill conditions.
 22. The method of claim 21 wherein the steps (A) through (D) are performed at least once every week.
 23. The method of claim 21 wherein the steps (A) through (D) are performed at least once on a daily basis.
 24. The method of claim 21 wherein the steps (A) through (D) are performed at least once on an hourly basis.
 25. The method of claim 21 wherein the steps (A) through (D) are performed at least once every fifteen minutes.
 26. The method of claim 21 wherein the steps (A) through (D) are performed at least once every five minutes.
 27. The method of claim 21 wherein both the amount and type of cement additives introduced into the grinding mill are adjusted.
 28. The method of claim 21 wherein the processor is programmed to adjust the introduction of chemical additives into the grinding mill in terms of type, formulation, amounts, dosage rate, or a combination thereof.
 29. The method of claim 28 wherein the processor is programmed to adjust the rate by which specific chemical additives are introduced into the grinding mill.
 30. The method of claim 21 wherein the processor is programmed to adjust cement additive dosage based on strength performance of cement, throughput of the mill, or combination thereof.
 31. The method of claim 21, wherein, a ground blend of particles or finished cement is sampled using an autosampler.
 32. Method of analyzing the performance of a ground finished cement, comprising: detecting, using an infrared sensor, at least one of emanation, reflectance, transmittance, or absorption of energy by or through ground finished particles of cement; comparing, using a processor, output signals provided by the infrared sensor system to data stored in processor-accessible memory, stored data previously obtained by detecting from ground finished cements by at least one sensor system, stored data correlated with a physical or chemical property of corresponding finished cement, hydrated cement or cementitious product made with the cement, namely, (i) strength test data, (ii) exothermic data; (iii) set initiation data; (iv) slump data; (v) dimensional stability data; (vi) air content data; (vii) pre-hydration level data, or; (viii) reduction or burn conditions data; (ix) cement particle size distribution data; and returning a predicted physical or chemical property of the corresponding finished cement.
 33. A cement provided by the method of claim
 21. 34. The method of claim 21 further comprising: (A) providing an indication chosen from audible alarm, visual alarm, monitor, hand-held display, text message, or email that a physical or chemical property or amount of the raw materials, raw meal, clinker, the source of calcium sulfate, the chemical additive, the SCM, or the finished cement has changed; (B) performing at least one test to determine a physical or chemical property of finished cement chosen from (i) strength test data, (ii) exothermic data, (iii) set initiation data, (iv) slump data, (v) dimensional stability data, (vi) air content data, (vii) prehydration level data, (viii) reduction or burn conditions data, (ix) cement particle size distribution data, or (x) a combination thereof; (C) detecting from finished cement tested in step (B) using at least one sensor system chosen from infrared sensor system, laser diffraction sensor system, or both, the at least one sensor system providing output signals corresponding to the reflectance, transmittance, or absorption of energy by or through the ground blend of particles or finished cement; (D) storing both the test results of step (B) and step (C) above, into a database accessible by a processor; and (E) making an adjustment to a model predicting at least one of (i) through (viii), making an adjustment to a target value for at least one of (i) through (viii), or making an adjustment to both the model and the target value.
 35. The method of claim 34 wherein the indication provided in step (A) is (i) a change in fuel source; (ii) a predefined deviation from a chemical property as measured by IR, LD, QXRD, XRF, PGNAA or a combination thereof, (iii) a predefined deviation in mill temperature or humidity, (iv) a predefined deviation in raw materials entering the kiln, (v) a change in a kiln processing condition, (vi) a change in a mill processing condition, or (vii) a notification that a manual or automated cement sample was taken.
 36. The method of claim 34 wherein testing in step (B) is done using an autosampler.
 37. The method of claim 34 wherein the indication is a change in any predicted value derived from a comparison between an IR signal and (i) strength test data, (ii) exothermic data; (iii) set initiation data; (iv) slump data; (v) dimensional stability data; (vi) air content data; (vii) prehydration level data; (viii) reduction or burn conditions data; (ix) cement particle size distribution data; or (x) a combination thereof.
 38. The method of claim 34 wherein an adjustment is made to a model predicting at least one of (i) through (ix) in step (B) based on NIR signal output value. 